Face Recognition using 3D Directional Corner Points
|
|
- Chad Caldwell
- 5 years ago
- Views:
Transcription
1 nd Internatonal Conference on Pattern Recognton Face Recognton usng 3D Drectonal Corner Ponts Xun Yu, Yongsheng Gao School of Engneerng Grffth Unversty Nathan, QLD, Australa Abstract In ths paper, we present a novel face recognton approach usng 3D drectonal corner ponts (3D DCPs). Tradtonally, ponts and meshes are appled to represent and match 3D shapes. Here we represent 3D surfaces by 3D DCPs derved from rdge and valley curves. Then we develop a 3D DCP matchng method to compute the smlarty of two dfferent 3D surfaces. Ths representaton, along wth the smlarty metrc can effectvely ntegrate structural and spatal nformaton on 3D surfaces. The added nformaton can provde more and better dscrmnatve power for obect recognton. It strengthens and mproves the matchng process of smlar 3D obects such as faces. To evaluate the performance of our method for 3D face recognton, we have performed experments on Face Recognton Grand Challenge v2.0 database (FRGC v2.0) and resulted n a ran-one recognton rate of 97.1%. Ths study demonstrates that 3D DCPs provdes a new soluton for 3D face recognton, whch may also fnd ts applcaton n general 3D obect representaton and recognton. Keywords3D drectonal corner ponts; 3D drectonal corner pont matchng; 3D face recognton I. INTRODUCTION Human target recognton has been an actve research area n the last decades, wth one of the maor topcs on automatc face detecton and matchng for the purpose of verfcaton and dentfcaton [1]. Sgnfcant achevements have been reached on two-dmensonal (2D) face recognton, but there are stll varous problems n handlng large amount of facal varances caused by changes n llumnaton, pose, expresson and age. Because the human face s a three-dmensonal (3D) obect whose 2D proecton s senstve to above changes. To overcome the nherent lmtatons assocated wth 2D face recognton technology, usng 3D face nformaton n face recognton has attracted ncreasng attenton, wth varous technques beng presented n recent years [2]. 3D face recognton s expected to be less senstve to llumnaton and pose varances because the 3D shape of a facal surface that s related to the nternal face anatomy nstead of external appearance and envronment. In addton, the geometrc nformaton avalable n 3D data s thought to provde more dscrmnatve features for face recognton. However, 3D face recognton technques also have ther own drawbacs. The conventonal methods [3] use holstc pont clouds and meshes on 3D face are computatonally expensve and n hgh storage demand. Therefore, t s crucal to fnd effcent and meanngful feature descrptors of 3D face Jun Zhou School of Informaton and Communcaton Technology Grffth Unversty Nathan, QLD, Australa Jun.zhou@grffth.edu.au structure to perform recognton. Mahoor and Mohamed [4] encoded the range data of 3D face nto a rdge mage, whch showed the locatons of rdge lnes around the mportant facal regons on the face (.e., eyes, nose, and mouth). Then teratve closet ponts (ICP) matchng method was utlzed to match the 3D ponts lyng on rdge mage of a gven probe to the created rdge mages of the subects n the gallery. In ther wor, only about 14% 2% of the total number of ponts on the range data were used, but acheved 91.8% accuracy n experments on FRGC v2.0 database. However, wthout consderng the nherent local structural characterstcs nsde such mages, ths method uses only the spatal nformaton of rdge mage. In ths paper, a novel 3D face descrpton and smlarty measurng technque s proposed, whch effectvely harnesses structural and spatal nformaton on a 3D surface, and reduces the storage requrement. Unle methods usng local operaton of solated ponts, the proposed approach employs 3D drectonal corner ponts (3D DCP) matchng n whch drectonal nformaton showng connectvty to ts neghbors s utlzed n the pont correspondence. Moreover, the storage load s further reduced by usng salent corner ponts nstead of all the ponts on those rdge and valley curves. The whole algorthm flowchart s llustrated n Fg. 1. In the followng, Secton 2 presents the proposed 3D drectonal corner ponts, whch ncorporates structural nformaton wth spatal features. Secton 3 presents our 3D drectonal corner ponts matchng method. Encouragng expermental results on a publc database are reported n Secton 4. Fnally, the paper concludes n Secton 5. Probe 3D Face Data Gallery Normalzed 3D Face Normalzed 3D Face Rdge and Valley Curves 3D Drectonal Corner Ponts Matchng Rdge and Valley Curves Fg. 1. Overvew of the proposed method 3D Drectonal Corner Ponts Identty 3D Drectonal Corner Ponts /14 $ IEEE DOI /ICPR
2 Fg. 2. Descrbng 3D shapes usng 3D drectonal corner ponts. The red ponts are 3D DCPs on rdge curves and the blue ponts are 3D DCPs on valley curves. II. 3D DIRECTIONAL CORNER POINTS In ths secton, we frst gve a bref ntroducton on the concept of prncple curvatures, based on whch we then propose 3D drectonal corner ponts as a descrptor for 3D shapes. For a gven pont on 3D face surface S, the maxmal and mnmal prncpal are max and, and ther correspondng mn prncpal drectons are denoted as t max and t. The mn defnton of ponts on rdge or valley curves n dfferental geometry are characterzed by e / t, e / t max max max mn mn mn e 0, e / t 0, ( rdges) max max max max mn e 0, e / t 0, ( valleys) (1) mn mn mn mn max As a curvature-based feature descrptor, rdge and valley curves on a 3D face surface along the eyes, the nose, and the mouth where the surface bends sharply are geometrcally and perceptually salent surface features, so they are expected to contan enough dscrmnatve nformaton for face recognton. In addton, codng 3D face nto rdge and valley can decrease the storage demand and computatonal expense. However, rdge (or valley) mage utlze spatal nformaton of 3D face but lac structural representaton. In ths study, we propose a 3D face feature descrptor, 3D drectonal corner ponts to solve ths problem, whch combnes the structural connectvty nformaton wth spatal nformaton of 3D faces. After detectng rdge and valley curves on 3D surface, a corner pont detecton process, whch s based on Douglas-Peucer algorthm [5], s then appled to generate 3D DCPs of the face. A 3D DCP, represented as P( x, y, z, n, n ), consst of Cartesan coordnates ( x, y, z ) and two unt drectonal vectors n 1 and n. n s the unt vector that ponts 2 1 to ts front neghborng corner pont. Smlarly, n 2 s the unt vector that ponts to ts rear neghborng corner pont. If a 3D DCP s a start pont of a curve, a null s assgned to n. If a 3D 1 DCP s an end pont of a curve, a null s assgned to n. A 3D 2 DCP s a two-drectonal corner pont wth two unt drectonal vectors pontng to ts two neghborng 3D DCPs or a onedrectonal corner pont (start/end pont) of the valley (or rdge) curves wth a sngle unt drectonal vector pontng to ts neghborng 3D DCP. These drectonal vectors provde solated feature ponts wth addtonal structural nformaton about the connectvty to ther neghbors, whch can enhance the dscrmnatve power of the descrptor. Moreover, the 3D DCP descrptor, usng spare ponts, further reduces the storage demand of a 3D shape representaton. Fg. 2 shows the 3D DCPs of an example 3D face n FRGC v2.0 database. 2803
3 Fg. 3. An llustraton of 3D DCP converson: (a) two 3D DCPs before convertng, (b) after translaton operaton, (c) after rotaton 1 operaton, (d) after rotaton 2 operaton, (e) after open/close operaton III. 3D DCP MATCHING After utlzng the 3D DCP detectng process above, a 3D face s encoded nto a set of 3D DCPs along rdge and valley curves, whch contan both poston and drecton features. A pont-to-pont convertng process s developed to calculate the dfference between two 3D DCPs from a face n probe database and a face n gallery database. The dssmlarty between the two faces s then calculated through a global convertng process between the two 3D DCP sets. A. Pont-to-pont Correspondence A A A A A B B B B B Let A( x, y, z, n, n ) and B( x, y, z, n, n ) be two 3D DCPs. The cost of convertng A to B (vce verse) s calculated through a four-step process that conssts of translaton operaton related to locaton feature, and rotaton and open/close operatons related to drecton feature. Fg. 3 llustrates the whole process where unt vector A A A A A B B B B B n ( n n ) / n n and n ( n n ) / n n are consdered as the prncpal drectonal vector of 3D DCPs A and B, whch are plotted n red and blue dashed lnes. To help demonstrate the whole convertng process, there are several B B A A planes plotted n Fg. 2: n n, n n, n n n A n B n B *, //., 1) Translaton operaton: A translaton operaton from A to B, denoted as T A B, moves A to the locaton of B, then x x, y y and z z (Fg. 3(a) and (b)). The cost functon for a translaton operaton from A to B s defned as ( ) ( ) ( ) (2) C T x x y y z z 2) Rotaton 1 operaton: A rotaton 1 operaton from A to B, denoted as R1 A B, rotates w.r.t to n A tll (Fg. 3(b) and (c)). The cost functon for a rotaton 1 operaton from A to B s defned as: C R A B 1 90 arccos( n n ) 90 arccos( ( n n ) ( n ( n n )) ) A A B where n and n are the normal vectors of planes and respectvely. 3) Rotaton 2 operaton: A rotaton 2 operaton from A to B, denoted as R2 A B, rotates w.r.t to n A tll (Fg. 3(c) and (d)). The cost functon for a rotaton 2 operaton from A to B s defned as: arccos (3) C R n n 2 (4) 2804
4 4) Open/Close operaton: An open (or close) operaton from A to B, denoted as O / C A B, opens (or closes) the two drectonal vectors of A untl the two drectonal vectors concde wth the correspondng drectonal vectors of B (Fg. 3(d) and (e)). The cost functon for an open/close operaton from A to B s defned as: B B A A C O / C arccos n n arccos n n (5) Let A B denote a convertng operaton from A to B. Equaton (6) defnes the cost functon for convertng A to B as an ntegrated cost of above four operatons. C R A B C A B C T A B f C R2 A B (6) C O / C A B where f ( x) s a non-lnear functon to penalze large angle devaton resulted from nter-class dfference, but gnore small varaton derved from segmentaton error or ntra-class dfference. In ths paper, a quadratc functon x f ( x) (7) W s used, where W s the weght to be determned by a tranng process and the delmt of f ( x ) s [0,450 ) as llustrated n Fg. 2. For converson between two one-drectonal 3D DCPs A A A A A( x, y, z, n ) and B( x B, y B, z B, n B ) ( = 1 or 2, = 1 or 2), the translaton operaton s the same as two-drectonal 3D DCPs, but the rotaton and open/close operatons are dfferent. A rotaton operaton from A to B rotates n A to n B. The cost functon for a rotaton operaton between two sngledrectonal 3D DCP s defned as arccos 2 C R n n (8) To arrange the drecton related operaton cost n the same range [0,450 ) as that of two-drecton 3D DCPs. The cost functon between two one-drectonal 3D DCPs s defned as C C T f C R (9) B. Set-to-set Correspondence We start from the defnton of two fnte 3D DCP sets r v r r r v v v G G G G{ A, A,, A, A, A,, A } and p q r v r r r v v v P P P P{ B, B,, B, B, B,, B } that represent a s t gallery and a probe n the 3D face database respectvely, where superscrpts r and v stand for rdge and valley. r v Therefore, G conssts of two subsets G and G that correspond to 3D DCPs along rdge and valley curves, and smlar settng apples to P. A 3D DCP set to set convertng process s proposed to establsh every 3D DCP correspondence between the two 3D DCP sets by mnmzng the global converson cost. For each 3D DCP A n G, ts correspondng 3D DCP B n P s dentfed as the one wth mnmum convertng cost from A to B among all B P ( represents r or v). The cost for establshng the correspondence for can be calculated by A C( A ) mn C( A B ) (11) B P Smlar to least trmmed square - HD (LTS - HD) [6] to handle the outler nose derved from the 3D scannng and corner detectng process, the cost for convertng the whole set G and P, denoted as G P, s defned as 1 1 C( G P ) C( A ) mn C( ) where H H H B 1 1 P H (12) H h N (0 h 1), N G G A G s the number of 3D DCP n G, and C( A ) are sorted n sequence C( A ) C( A ) C( A N ). The measure s calculated by elmnatng the large converson cost values and only eepng the h fracton of the smallest convertng cost. In ths study, the value of h s set as 0.8, whch results the best recognton performance. Fnally, the dssmlarty between G and P s defned as the maxmum of the two mnmum costs to establsh correspondences from G to P and vce versa. In (13), the dssmlarty costs between rdge and valley subsets are calculated frst, then the fnal result s merged from the two subset dssmlartes. Ths fuson process can further mprove the dscrmnatve power. For convertng between a one-drectonal 3D DCP and a two-drectonal 3D DCP, the translaton operaton s the same, but the maxmum value 450 s set as the drecton related operaton cost. Because t s desrable to prohbt convertng between two 3D DCPs of dfferent types. The cost functon between two one-drectonal 3D DCPs s defned as (10) C C T f r r v v D( G, P) D( G, P ) D( G, P ) r r r r max[ C( G P ), C( P G )] v v v v max[ C( G P ), C( P G )] (13) 2805
5 Fg. 5. The effect of W on recognton rate. Fg. 4. Sample 3D faces used n our experments. (a) Normalzed faces, (b) Rdge data, (c) Valley data. IV. EXPERIMENTS AND RESULTS The FRGC v2.0 [7] 3D face database was used n our experments to evaluate the feasblty and effectveness of the proposed approach. The FRGC v2.0 database contans 4950 face texture and range mages, dvded nto three sets, namely Sprng2003, Fall2004 and Sprng2004. In lne wth FRGC v2.0 protocol, n our experments, the tranng set was generated from Sprng2003 and the test mages were generated from the other two sets. The face mages are extracted, normalzed and cropped n the same manner as n [8]. Spes n the range maps are removed and holes are flled. Fg. 4 llustrates several samples of normalzed and cropped face mages n the FRGC v2.0 dataset. A. Determnaton of Parameter W In ths secton, we nvestgate the effect of parameter W n (7) on the recognton accuracy. In ths experment, 100 people wth 2 neutral 3D faces per person from the FRGC v2.0 Sprng2003 were used to create a tranng dataset. The neutral 3D face n sesson one were used to construct the gallery database, and the neutral 3D face n sesson two were used as probe mages. The recognton rate s plotted aganst the values of W n Fg. 5. It s observed that the algorthm performed badly wth a low value of W and only acheved 7.2% when W = 10. The performance ncreased qucly and reached the optmal value of 98% when W was 600 and remaned stable tll In the rest of experments n ths study, W was set as Fg. 6. CMC curves of the proposed approach B. Face Recognton In ths experment, we nvestgated the effectveness of the proposed approach on the neutral 3D face mages of the FRGC v2.0 database. There are 388 subects whch have at least two neutral mages captured n dfferent sessons. However, some mages were found lost or corrupted after downloadng through Internet. 385 subects are complete and can be used. For each subect, one neutral 3D face was used as a gallery whle the other was used as a probe. The performance s measured n terms of the Cumulatve Match Characterstcs (CMC) [9] and the ran-1 recognton rate. In order to demonstrate the performance mprovement resulted from the fuson process, we also presented the fnal fuson result, as well as the result based on rdge (or valley) data only, as llustrated n Fg. 6. It s encouragng to fnd that the fuson process mprove the recognton performance greatly. The ran-1 recognton rate of the methods gven n Fg. 6 above are tabled n Table 1 together wth the reported results of Mahoor and Mohamed [4]. Note our methods perform consstently superor to the benchmar method based 2806
6 TABLE 1. RECOGNITION ACCURACIES ON 3D FACE SCANS Method Recognton Result Pont Number* 3D DCP (fuson result) 97.1% 5.9% 3D DCP (rdge data) 95.1% 3.7% 3D DCP (valley data) 92.7% 2.2% Mahoor and Mohamed [4] (rdge mage) Mahoor and Mohamed [4] (entre surface) 91.8% 14% 93.7% 100% * Pont number s calculated through the percentage of the entre 3D face scans. [5] D. H. Douglas and T. K. Peucer, "Algorthms for the reducton of the number of ponts requred to represent a dgtzed lne or ts carcature," Cartographca: The Internatonal Journal for Geographc Informaton and Geovsualzaton, vol. 10, pp , [6] D. G. Sm, O. K. Kwon, and R. H. Par, "Obect matchng algorthms usng robust Hausdorff dstance measures," Ieee Transactons on Image Processng, vol. 8, pp , [7] P. J. Phllps, et al., "Overvew of the face recognton grand challenge," n Proceedngs of IEEE Computer Socety Conference on Computer Vson and Pattern Recognton, pp , [8] A. S. Man, M. Bennamoun, and R. Owens, "An effcent multmodal 2D-3D hybrd approach to automatc face recognton," IEEE Transactons on Pattern Analyss and Machne Intellgence, vol. 29, pp , [9] S. A. Rzv, P. J. Phllps, and H. Moon, "The FERET verfcaton testng protocol for face recognton algorthms," n Proceedngs of the 3rd Internatonal Conference on Automatc Face and Gesture Recognton, pp , on rdge mage, whch demonstrates that the added structural nformaton maes 3D DCP a more dscrmnatve measure than tradtonal feature pont based method. The fuson result of our method s also hgher than Mahoor and Mohamed [4] on the entre surface by 3.4%. In addton, compared wth the benchmar that usng entre surface, our 3D DCP methods based on rdge data, valley data and fuson process, requre only about 3.7%, 2.2% and 5.9 % storage space, whch further decreases the storage demand. V. CONCLUSIONS Ths paper presents a new 3D face recognton method usng 3D drectonal corner ponts (3D DCPs), whch employs both spatal and structural nformaton of rdge and valley curves on 3D surface. In order to represent the 3D shape effcently, we extract 3D drectonal corner ponts from rdge and valley curves. Such sparse pont representaton can further reduce the storage demand and the drectonal attrbutes can effectvely enhance the dscrmnatve power. The proposed method has been evaluated on the FRGV v2 dataset and been compared wth a benchmar approach based on rdge mage. It s very encouragng to fnd that the 3D DCP method performed superor to the benchmar approach n terms of hgher recognton accuracy and less storage space demand. Ths study reveals that 3D DCPs provdes a new soluton for 3D face recognton, whch may also fnd ts applcaton n general 3D obect representaton and recognton. REFERENCES [1] W. Zhao, R. Chellappa, P. J. Phllps, and A. Rosenfeld, "Face recognton: A lterature survey," ACM Computng Surveys, vol. 35, pp , [2] K. W. Bowyer, K. Chang, and P. Flynn, "A survey of approaches and challenges n 3D and mult-modal 3D+2D face recognton," Computer Vson and Image Understandng, vol. 101, pp. 1-15, [3] G. Medon and R. Waupottsch, "Face modelng and recognton n 3- D," n Proceedngs of IEEE Internatonal Worshop on Analyss and Modelng of Face and Gestures, pp , [4] M. H. Mahoor and M. Abdel-Mottaleb, "Face recognton based on 3D rdge mages obtaned from range data," Pattern Recognton, vol. 42, pp ,
Gender Classification using Interlaced Derivative Patterns
Gender Classfcaton usng Interlaced Dervatve Patterns Author Shobernejad, Ameneh, Gao, Yongsheng Publshed 2 Conference Ttle Proceedngs of the 2th Internatonal Conference on Pattern Recognton (ICPR 2) DOI
More informationLocal Quaternary Patterns and Feature Local Quaternary Patterns
Local Quaternary Patterns and Feature Local Quaternary Patterns Jayu Gu and Chengjun Lu The Department of Computer Scence, New Jersey Insttute of Technology, Newark, NJ 0102, USA Abstract - Ths paper presents
More informationShape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram
Shape Representaton Robust to the Sketchng Order Usng Dstance Map and Drecton Hstogram Department of Computer Scence Yonse Unversty Kwon Yun CONTENTS Revew Topc Proposed Method System Overvew Sketch Normalzaton
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationFEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur
FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents
More informationRecognizing Faces. Outline
Recognzng Faces Drk Colbry Outlne Introducton and Motvaton Defnng a feature vector Prncpal Component Analyss Lnear Dscrmnate Analyss !"" #$""% http://www.nfotech.oulu.f/annual/2004 + &'()*) '+)* 2 ! &
More informationContent Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers
IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth
More informationOutline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1
4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:
More informationHierarchical clustering for gene expression data analysis
Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationDetection of an Object by using Principal Component Analysis
Detecton of an Object by usng Prncpal Component Analyss 1. G. Nagaven, 2. Dr. T. Sreenvasulu Reddy 1. M.Tech, Department of EEE, SVUCE, Trupath, Inda. 2. Assoc. Professor, Department of ECE, SVUCE, Trupath,
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationImage Alignment CSC 767
Image Algnment CSC 767 Image algnment Image from http://graphcs.cs.cmu.edu/courses/15-463/2010_fall/ Image algnment: Applcatons Panorama sttchng Image algnment: Applcatons Recognton of object nstances
More informationAn Image Fusion Approach Based on Segmentation Region
Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua
More informationMachine Learning: Algorithms and Applications
14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of
More informationFace Recognition Based on SVM and 2DPCA
Vol. 4, o. 3, September, 2011 Face Recognton Based on SVM and 2DPCA Tha Hoang Le, Len Bu Faculty of Informaton Technology, HCMC Unversty of Scence Faculty of Informaton Scences and Engneerng, Unversty
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationDiscriminative Dictionary Learning with Pairwise Constraints
Dscrmnatve Dctonary Learnng wth Parwse Constrants Humn Guo Zhuoln Jang LARRY S. DAVIS UNIVERSITY OF MARYLAND Nov. 6 th, Outlne Introducton/motvaton Dctonary Learnng Dscrmnatve Dctonary Learnng wth Parwse
More informationSubspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;
Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features
More informationCorner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity
Journal of Sgnal and Informaton Processng, 013, 4, 114-119 do:10.436/jsp.013.43b00 Publshed Onlne August 013 (http://www.scrp.org/journal/jsp) Corner-Based Image Algnment usng Pyramd Structure wth Gradent
More informationMULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION
MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More informationNovel Fuzzy logic Based Edge Detection Technique
Novel Fuzzy logc Based Edge Detecton Technque Aborsade, D.O Department of Electroncs Engneerng, adoke Akntola Unversty of Tech., Ogbomoso. Oyo-state. doaborsade@yahoo.com Abstract Ths paper s based on
More informationModular PCA Face Recognition Based on Weighted Average
odern Appled Scence odular PCA Face Recognton Based on Weghted Average Chengmao Han (Correspondng author) Department of athematcs, Lny Normal Unversty Lny 76005, Chna E-mal: hanchengmao@163.com Abstract
More informationPalmprint Feature Extraction Using 2-D Gabor Filters
Palmprnt Feature Extracton Usng 2-D Gabor Flters Wa Kn Kong Davd Zhang and Wenxn L Bometrcs Research Centre Department of Computng The Hong Kong Polytechnc Unversty Kowloon Hong Kong Correspondng author:
More informationA Fast Visual Tracking Algorithm Based on Circle Pixels Matching
A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng
More informationComputer Animation and Visualisation. Lecture 4. Rigging / Skinning
Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume
More informationObject-Based Techniques for Image Retrieval
54 Zhang, Gao, & Luo Chapter VII Object-Based Technques for Image Retreval Y. J. Zhang, Tsnghua Unversty, Chna Y. Y. Gao, Tsnghua Unversty, Chna Y. Luo, Tsnghua Unversty, Chna ABSTRACT To overcome the
More informationMargin-Constrained Multiple Kernel Learning Based Multi-Modal Fusion for Affect Recognition
Margn-Constraned Multple Kernel Learnng Based Mult-Modal Fuson for Affect Recognton Shzh Chen and Yngl Tan Electrcal Engneerng epartment The Cty College of New Yor New Yor, NY USA {schen, ytan}@ccny.cuny.edu
More informationSteps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices
Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between
More informationA Novel Adaptive Descriptor Algorithm for Ternary Pattern Textures
A Novel Adaptve Descrptor Algorthm for Ternary Pattern Textures Fahuan Hu 1,2, Guopng Lu 1 *, Zengwen Dong 1 1.School of Mechancal & Electrcal Engneerng, Nanchang Unversty, Nanchang, 330031, Chna; 2. School
More informationFuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches
Proceedngs of the Internatonal Conference on Cognton and Recognton Fuzzy Flterng Algorthms for Image Processng: Performance Evaluaton of Varous Approaches Rajoo Pandey and Umesh Ghanekar Department of
More informationEfficient Segmentation and Classification of Remote Sensing Image Using Local Self Similarity
ISSN(Onlne): 2320-9801 ISSN (Prnt): 2320-9798 Internatonal Journal of Innovatve Research n Computer and Communcaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol.2, Specal Issue 1, March 2014 Proceedngs
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationA Robust Method for Estimating the Fundamental Matrix
Proc. VIIth Dgtal Image Computng: Technques and Applcatons, Sun C., Talbot H., Ourseln S. and Adraansen T. (Eds.), 0- Dec. 003, Sydney A Robust Method for Estmatng the Fundamental Matrx C.L. Feng and Y.S.
More informationAn efficient method to build panoramic image mosaics
An effcent method to buld panoramc mage mosacs Pattern Recognton Letters vol. 4 003 Dae-Hyun Km Yong-In Yoon Jong-Soo Cho School of Electrcal Engneerng and Computer Scence Kyungpook Natonal Unv. Abstract
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationEYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS
P.G. Demdov Yaroslavl State Unversty Anatoly Ntn, Vladmr Khryashchev, Olga Stepanova, Igor Kostern EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS Yaroslavl, 2015 Eye
More informationFace Recognition University at Buffalo CSE666 Lecture Slides Resources:
Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural
More information2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements
Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationScale Selective Extended Local Binary Pattern For Texture Classification
Scale Selectve Extended Local Bnary Pattern For Texture Classfcaton Yutng Hu, Zhlng Long, and Ghassan AlRegb Multmeda & Sensors Lab (MSL) Georga Insttute of Technology 03/09/017 Outlne Texture Representaton
More informationA Background Subtraction for a Vision-based User Interface *
A Background Subtracton for a Vson-based User Interface * Dongpyo Hong and Woontack Woo KJIST U-VR Lab. {dhon wwoo}@kjst.ac.kr Abstract In ths paper, we propose a robust and effcent background subtracton
More informationMulti-view 3D Position Estimation of Sports Players
Mult-vew 3D Poston Estmaton of Sports Players Robbe Vos and Wlle Brnk Appled Mathematcs Department of Mathematcal Scences Unversty of Stellenbosch, South Afrca Emal: vosrobbe@gmal.com Abstract The problem
More informationCollaboratively Regularized Nearest Points for Set Based Recognition
Academc Center for Computng and Meda Studes, Kyoto Unversty Collaboratvely Regularzed Nearest Ponts for Set Based Recognton Yang Wu, Mchhko Mnoh, Masayuk Mukunok Kyoto Unversty 9/1/013 BMVC 013 @ Brstol,
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationOn Modeling Variations For Face Authentication
On Modelng Varatons For Face Authentcaton Xaomng Lu Tsuhan Chen B.V.K. Vjaya Kumar Department of Electrcal and Computer Engneerng, Carnege Mellon Unversty Abstract In ths paper, we present a scheme for
More informationDetection of Human Actions from a Single Example
Detecton of Human Actons from a Sngle Example Hae Jong Seo and Peyman Mlanfar Electrcal Engneerng Department Unversty of Calforna at Santa Cruz 1156 Hgh Street, Santa Cruz, CA, 95064 {rokaf,mlanfar}@soe.ucsc.edu
More informationPrivate Information Retrieval (PIR)
2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationFeature-based image registration using the shape context
Feature-based mage regstraton usng the shape context LEI HUANG *, ZHEN LI Center for Earth Observaton and Dgtal Earth, Chnese Academy of Scences, Bejng, 100012, Chna Graduate Unversty of Chnese Academy
More information12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification
Introducton to Artfcal Intellgence V22.0472-001 Fall 2009 Lecture 24: Nearest-Neghbors & Support Vector Machnes Rob Fergus Dept of Computer Scence, Courant Insttute, NYU Sldes from Danel Yeung, John DeNero
More informationVectorization of Image Outlines Using Rational Spline and Genetic Algorithm
01 Internatonal Conference on Image, Vson and Computng (ICIVC 01) IPCSIT vol. 50 (01) (01) IACSIT Press, Sngapore DOI: 10.776/IPCSIT.01.V50.4 Vectorzaton of Image Outlnes Usng Ratonal Splne and Genetc
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationHigh-Boost Mesh Filtering for 3-D Shape Enhancement
Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,
More informationFitting & Matching. Lecture 4 Prof. Bregler. Slides from: S. Lazebnik, S. Seitz, M. Pollefeys, A. Effros.
Fttng & Matchng Lecture 4 Prof. Bregler Sldes from: S. Lazebnk, S. Setz, M. Pollefeys, A. Effros. How do we buld panorama? We need to match (algn) mages Matchng wth Features Detect feature ponts n both
More informationRange images. Range image registration. Examples of sampling patterns. Range images and range surfaces
Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 15
CS434a/541a: Pattern Recognton Prof. Olga Veksler Lecture 15 Today New Topc: Unsupervsed Learnng Supervsed vs. unsupervsed learnng Unsupervsed learnng Net Tme: parametrc unsupervsed learnng Today: nonparametrc
More informationA COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION
ISSN: 0976-910(ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 013, VOLUME: 03, ISSUE: 04 A COMBINED APPROACH USING TEXTURAL AND GEOMETRICAL FEATURES FOR FACE RECOGNITION A. Suruland 1, R. Reena
More informationSkew Angle Estimation and Correction of Hand Written, Textual and Large areas of Non-Textual Document Images: A Novel Approach
Angle Estmaton and Correcton of Hand Wrtten, Textual and Large areas of Non-Textual Document Images: A Novel Approach D.R.Ramesh Babu Pyush M Kumat Mahesh D Dhannawat PES Insttute of Technology Research
More informationQuerying by sketch geographical databases. Yu Han 1, a *
4th Internatonal Conference on Sensors, Measurement and Intellgent Materals (ICSMIM 2015) Queryng by sketch geographcal databases Yu Han 1, a * 1 Department of Basc Courses, Shenyang Insttute of Artllery,
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationFace Recognition by Fusing Binary Edge Feature and Second-order Mutual Information
Face Recognton by Fusng Bnary Edge Feature and Second-order Mutual Informaton Jatao Song, Bejng Chen, We Wang, Xaobo Ren School of Electronc and Informaton Engneerng, Nngbo Unversty of Technology Nngbo,
More informationSemantic Image Retrieval Using Region Based Inverted File
Semantc Image Retreval Usng Regon Based Inverted Fle Dengsheng Zhang, Md Monrul Islam, Guoun Lu and Jn Hou 2 Gppsland School of Informaton Technology, Monash Unversty Churchll, VIC 3842, Australa E-mal:
More informationFinger-Vein Verification Based on Multi-Features Fusion
Sensors 203, 3, 5048-5067; do:0.3390/s35048 Artcle OPEN ACCESS sensors ISSN 424-8220 www.mdp.com/journal/sensors Fnger-Ven Verfcaton Based on Mult-Features Fuson Huafeng Qn,2, *, Lan Qn 2, Lan Xue 2, Xpng
More informationA PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION
1 THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 4, Number 2/2003, pp.000-000 A PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION Tudor BARBU Insttute
More informationIntegrated Expression-Invariant Face Recognition with Constrained Optical Flow
Integrated Expresson-Invarant Face Recognton wth Constraned Optcal Flow Chao-Kue Hseh, Shang-Hong La 2, and Yung-Chang Chen Department of Electrcal Engneerng, Natonal Tsng Hua Unversty, Tawan 2 Department
More informationTerm Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task
Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto
More informationA Novel Fingerprint Matching Method Combining Geometric and Texture Features
A Novel ngerprnt Matchng Method Combnng Geometrc and Texture eatures Me Xe, Chengpu Yu and Jn Q Unversty of Electronc Scence and Technology of Chna. Chengdu,P.R.Chna xeme@ee.uestc.edu.cn Post Code:6154
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationKIDS Lab at ImageCLEF 2012 Personal Photo Retrieval
KD Lab at mageclef 2012 Personal Photo Retreval Cha-We Ku, Been-Chan Chen, Guan-Bn Chen, L-J Gaou, Rong-ng Huang, and ao-en Wang Knowledge, nformaton, and Database ystem Laboratory Department of Computer
More informationObject Tracking Based on PISC Image and Template Matching
ect Trackng Based on PISC Image and Template Matchng Bud Sugand Electrcal Engneerng Department Batam State Polytechnc Batam Indonesa ud_sugand@polatam.ac.d Astract Ths paper proposed a method for oect
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationWireless Sensor Network Localization Research
Sensors & Transducers 014 by IFSA Publshng, S L http://wwwsensorsportalcom Wreless Sensor Network Localzaton Research Lang Xn School of Informaton Scence and Engneerng, Hunan Internatonal Economcs Unversty,
More informationTPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints
TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationSum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints
Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan
More informationModeling Inter-cluster and Intra-cluster Discrimination Among Triphones
Modelng Inter-cluster and Intra-cluster Dscrmnaton Among Trphones Tom Ko, Bran Mak and Dongpeng Chen Department of Computer Scence and Engneerng The Hong Kong Unversty of Scence and Technology Clear Water
More informationTone-Aware Sparse Representation for Face Recognition
Tone-Aware Sparse Representaton for Face Recognton Lngfeng Wang, Huayu Wu and Chunhong Pan Abstract It s stll a very challengng task to recognze a face n a real world scenaro, snce the face may be corrupted
More informationFacial Expressions Recognition in a Single Static as well as Dynamic Facial Images Using Tracking and Probabilistic Neural Networks
Facal Expressons Recognton n a Sngle Statc as well as Dynamc Facal Images Usng Trackng and Probablstc Neural Networks Had Seyedarab 1, Won-Sook Lee 2, Al Aghagolzadeh 1, and Sohrab Khanmohammad 1 1 Faculty
More informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
More informationA Deflected Grid-based Algorithm for Clustering Analysis
A Deflected Grd-based Algorthm for Clusterng Analyss NANCY P. LIN, CHUNG-I CHANG, HAO-EN CHUEH, HUNG-JEN CHEN, WEI-HUA HAO Department of Computer Scence and Informaton Engneerng Tamkang Unversty 5 Yng-chuan
More informationSupport Vector Machines
Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned
More informationDynamic wetting property investigation of AFM tips in micro/nanoscale
Dynamc wettng property nvestgaton of AFM tps n mcro/nanoscale The wettng propertes of AFM probe tps are of concern n AFM tp related force measurement, fabrcaton, and manpulaton technques, such as dp-pen
More informationLearning a Class-Specific Dictionary for Facial Expression Recognition
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 4 Sofa 016 Prnt ISSN: 1311-970; Onlne ISSN: 1314-4081 DOI: 10.1515/cat-016-0067 Learnng a Class-Specfc Dctonary for
More informationAPPLIED MACHINE LEARNING
Methods for Clusterng K-means, Soft K-means DBSCAN 1 Objectves Learn basc technques for data clusterng K-means and soft K-means, GMM (next lecture) DBSCAN Understand the ssues and major challenges n clusterng
More informationUnsupervised Learning and Clustering
Unsupervsed Learnng and Clusterng Supervsed vs. Unsupervsed Learnng Up to now we consdered supervsed learnng scenaro, where we are gven 1. samples 1,, n 2. class labels for all samples 1,, n Ths s also
More informationA mathematical programming approach to the analysis, design and scheduling of offshore oilfields
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and
More informationCOMPLEX WAVELET TRANSFORM-BASED COLOR INDEXING FOR CONTENT-BASED IMAGE RETRIEVAL
COMPLEX WAVELET TRANSFORM-BASED COLOR INDEXING FOR CONTENT-BASED IMAGE RETRIEVAL Nader Safavan and Shohreh Kasae Department of Computer Engneerng Sharf Unversty of Technology Tehran, Iran skasae@sharf.edu
More informationRobust Face Recognition through Local Graph Matching
Robust Face Recognton through Local Graph Matchng Ehsan Fazl-Ers, John S. Zele and John K. Tsotsos, Department of Computer Scence and Engneerng, Yor Unversty, Toronto, Canada E-Mal: [efazl, tsotsos]@cse.yoru.ca
More informationNovel Pattern-based Fingerprint Recognition Technique Using 2D Wavelet Decomposition
Mathematcal Methods for Informaton Scence and Economcs Novel Pattern-based Fngerprnt Recognton Technque Usng D Wavelet Decomposton TUDOR BARBU Insttute of Computer Scence of the Romanan Academy T. Codrescu,,
More informationA MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS
Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung
More informationAUTOMATED personal identification using biometrics
A 3D Feature Descrptor Recovered from a Sngle 2D Palmprnt Image Qan Zheng,2, Ajay Kumar, and Gang Pan 2 Abstract Desgn and development of effcent and accurate feature descrptors s crtcal for the success
More informationFacial Expression Recognition Based on Local Binary Patterns and Local Fisher Discriminant Analysis
WSEAS RANSACIONS on SIGNAL PROCESSING Shqng Zhang, Xaomng Zhao, Bcheng Le Facal Expresson Recognton Based on Local Bnary Patterns and Local Fsher Dscrmnant Analyss SHIQING ZHANG, XIAOMING ZHAO, BICHENG
More informationHistogram of Template for Pedestrian Detection
PAPER IEICE TRANS. FUNDAMENTALS/COMMUN./ELECTRON./INF. & SYST., VOL. E85-A/B/C/D, No. xx JANUARY 20xx Hstogram of Template for Pedestran Detecton Shaopeng Tang, Non Member, Satosh Goto Fellow Summary In
More informationLEAST SQUARES. RANSAC. HOUGH TRANSFORM.
LEAS SQUARES. RANSAC. HOUGH RANSFORM. he sldes are from several sources through James Has (Brown); Srnvasa Narasmhan (CMU); Slvo Savarese (U. of Mchgan); Bll Freeman and Antono orralba (MI), ncludng ther
More informationRobust Low-Rank Regularized Regression for Face Recognition with Occlusion
Robust Low-Rank Regularzed Regresson for ace Recognton wth Occluson Janjun Qan, Jan Yang, anlong Zhang and Zhouchen Ln School of Computer Scence and ngneerng, Nanjng Unversty of Scence and echnology Key
More information