Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features
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1 Journal of Software Engneerng and Applcatons, 2013, 6, Publshed Onlne October 2013 ( 519 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features Mohamed A. El-Sayed 1,2, Nora G. Ahmed 3 1 Department of Mathematcs, Faculty of Scence, Fayoum Unversty, Al Fayoum, Egypt; 2 Department of Computer Scence, Taf Unversty, Al Hawyah, KSA; 3 Department of Mathematcs, Faculty of Scence, Sohag Unversty, Sohag, Egypt. Emal: mas06@fayoum.edu.eg Receved August 2 nd, 2013; revsed September 1 st, 2013; accepted September 8 th, 2013 Copyrght 2013 Mohamed A. El-Sayed, Nora G. Ahmed. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. ABSTRACT Face detecton s consdered as a challengng problem n the feld of mage analyss and computer vson. There are many researches n ths area, but because of ts mportance, t needs to be further developed. Successve Mean Quantzaton Transform (SMQT) for llumnaton and sensor nsenstve operaton and Sparse Network of Wnnow (SNoW) to speed up the orgnal classfer based detecton technque presented such a good result. In ths paper we use the Mean of Medans of CbCr (MMCbCr) color correcton approach to enhance the combned SMQT features and SNoW classfer detecton technque. The proposed technque s appled on color mages gathered from varous sources such as Internet, and Georga Database. Expermental results show that the detecton performance of the proposed method s more effectve and accurate compared to SFSC method. Keywords: Face Detecton; Color Correcton; MMCbCr; SMQT Features 1. Introducton Face detecton s a computer technology that determnes the locatons and szes of human s n dgtal mages. It detects facal features and gnores anythng else, such as buldngs, trees and bodes [1-3]. In recent years, recognton has attracted much attenton and ts research has rapdly expanded by not only engneers but also neuroscentsts, snce t has many potental applcatons n computer vson communcaton and automatc access control system. Especally, detecton s an mportant part of recognton as the frst step of automatc recognton. However, detecton s not straghtforward because t has lots of varatons of mage appearance, such as pose varaton (front, non-front), occluson, mage orentaton, llumnatng condton and facal expresson [4,5]. Up to now, much work has been done n detectng and locatng s n mages and there are many detecton methods, such as SMQT Features and SNoW Classfer Method (SFSC) [6], Effcent and Rank Defcent Face Detecton Method (ERDFD) [7], Gabor-Feature Extracton and Neural Network Method (GFENN) [8], an effcent canddates selector Features Method (EFCSF) [9] and Neural network based [10]. Colors of the mages provde useful nformaton for many vson applcatons. As a result, dfferent cameras typcally produce dfferent color values for the same objects or scenes, as llustrated n Fgure 1. These dfferences complcate the task of computer vson applcatons nvolvng the use of more than one camera. A color correcton approach s thus requred to correct the mages so that colors of the same object appear to be smlar n the output from each camera [11]. There were a number of Color Correcton approaches, ncludng GW approach, WP approach, MGWWP approach, Stretch approach and MMCbCr approach [12]. In ths paper, for detecton we use (SFSC) method: local SMQT features whch can be used as feature extracton for object and SNoW classfer requre for tranng. But we found that we can enhance ths method by applyng MMCbCr Color Correcton approach on the nput mages that make the process of detecton better. The outlne of the paper s as follows. Introducton about detecton methods s presented n Secton 1. Secton 2 dscusses the challenges on detecton technques. Secton 3 explans the proposed method that uses color correcton approach to enhance SFSC
2 520 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features detecton method. Secton 4 descrbes the stage of local SMQT features. Secton 5 presents the concept of splt up SNoW classfer. Secton 6 explans the detecton tranng and classfcaton. In Secton 7, we have presented the effectveness of proposed algorthm. The proposed technque s appled on color mages gathered from varous sources such as Internet, UCD Face Image Database and Georga Database. Also, we compare the results of the algorthm wth SFSC method. Conclusons are presented n Secton Challenges on Face Detecton Technques The problem s further complcated by dfferng lghtng condtons, mage qualtes and geometres, as well as the possblty of partal occluson and dsguse. An deal detector would therefore be able to detect the presence of any under any set of lghtng condtons, orentaton, and camera dstance upon any background. Mng-Hsuan, et al. [1] Summarze the challenges assocated wth detecton n the followng factors: 1) Pose: the mages of a vary due to the relatve camera- pose (frontal, 45 degree, profle, upsde down), and some facal features such as an eye or the nose may become partally or wholly occluded. 2) Presence or absence of structural components: facal features such as beards, mustaches, and glasses may or may not be present and there s a great deal of varablty among these components ncludng shape, color, and sze. 3) Facal expresson: the appearance of s s drectly affected by a person s facal expresson. 4) Occluson: s may be partally occluded by other objects. In an mage wth a group of people, some s may partally occlude other s. 5) Image orentaton: mages drectly vary for dfferent rotatons about the camera s optcal axs. 6) Imagng condtons: when the mage s formed, factors such as lghtng (spectra, source dstrbuton and ntensty) and camera characterstcs (sensor response, lenses) may change appearance n the mage. Image condton ncludes also sze, lghtng condton, dstorton, nose, and compresson. 7) Face Sze: Sze of s also make dffcult to automate a system for detecton and recognton. 8) A background varaton: s another challengng factor for detecton n cluttered scenes. Dscrmnatng wndows ncludng a from non- s more dffcult when no constrants exst on background. Some closely related problems of detecton [1]: 1) Face localzaton: ams to determne the mage poston of a sngle ; ths s a smplfed detecton problem wth the assumpton that an nput mage contans only one. 2) Face recognton or dentfcaton: compares an nput mage (probe) aganst a database (gallery) and reports a match, f any. 3) Face authentcaton s to verfy the clam of the dentty of an ndvdual n an nput mage. 4) Face trackng methods contnuously estmate the locaton and possbly the orentaton of a n an mage sequence n real tme. 5) Facal expresson recognton concerns dentfyng the affectve states (happy, sad, dsgusted, etc.) of humans. 6) Feature s used to denote a pece of nformaton whch s relevant for solvng the computatonal task related to a certan applcaton. Feature s measurable heurstc propertes of the phenomena beng observed. 3. Proposed Method In the proposed method, the goal s to detect the presence of s n an mage usng MMCbCr Color Correcton approach and SFSC method to detect s unform and non-unform background color of the scene. It s able to localze s wth dfferent szes n mages taken under varyng llumnaton condtons. The phases of the proposed method as llustrated n Fgure Color Correcton Phase In ths phase we use Mean of Medans of CbCr Color Correcton approach (MMCbCr) to correct the nput mages. The Y component contans the lumnance nformaton and the chromnance nformaton s found n the chromnance blue Cb and n the chromnance red Cr. The MMCbCr Color Correcton SFSC Face Detecton Fgure 1. Images captured by three dfferent cameras. Fgure 2. The phases of proposed method.
3 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features 521 RGB components were converted to the YCbCr components usng the followng formula [12,13]. Y 0.257R 0.504G 0.098B 16 Cb 0.148R 0.291G 0.439B 128 Cr 0.439R 0.368G 0.071B 128 The followng steps summarze MMCbCr approach: 1) Transform the gven mage from RGB to YCbCr color model. 2) Calculate the medan values medan (Cb), medan (Cr) for Cb and Cr color component, and maxmum value max(y) n Y. 3) Calculate the mean values mean (Cb), mean (Cr) for Cb and Cr color component. 4) an Cr Value Medan Cb Med 2. 5) For all pxels of the mage calculate Y new, Cb new, and Cr new j j j j j j Y, Y, 235 Max Y new new Cb, Cb, Value Mean Cb Cr, Cr, Value Mean Cr new 6) Transform the mage components Y new, Cb new, and Cr new to R new G new B new. 7) Apply hstogram equalzaton on R new G new B new separately. 8) Combne R new G new B new to get the fnal color mage Face Detecton Phase In ths phase, we use SFSC method to localzng s n nput mages. Here there are three stages: 1) Local SMQT features whch can be used as feature extracton for object, 2) SNoW classfer requres for tranng, and 3) Face detecton Tranng and Classfcaton. 4. Local SMQT Features The SMQT performs an automatc structural breakdown of nformaton. These propertes wll be employed on local areas n an mage to extract llumnaton nsenstve features. Local areas can be defned n several ways. Once the local area s defned t wll be a set of pxel values. SMQTL :Dx Μ x (1) where x be one pxel and D(x) be a set of D(x) = D be pxels n local area n an mage. The resultng values are nsenstve to gan and bas. These propertes are desrable wth regard to the formaton of the whole ntensty mage I(x) whch s a product of the reflectance R(x) and the lumnance E(x). Addtonally, the nfluence of the camera can be modeled as a gan factor g and a bas term b [14]. Thus, a model of the mage can be descrbed by I x ge xrx b (2) In order to desgn a robust classfer for object detecton the reflectance should be extracted snce t contans the object structure. In general, the separaton of the reflectance and the lumnance s an ll posed problem. A common approach to solvng ths problem nvolves assumng that E(x) s spatally smooth. Archtecture Further, f the lumnance can be consdered to be constant n the chosen local area then E(x) s gven by E x E, x D (3) Gven the valdty of Equaton (3), the SMQT on the local area wll yeld llumnaton and camera-nsenstve features. Ths mples that all local patterns whch contan the same structure wll yeld the same SMQT features for a specfed level L. 5. Splt up SNoW Classfer The SNoW learnng s a sparse network of lnear unts over a feature space. One of the strong propertes of SNoW s the possblty to create lookup-tables for classfcaton. Consder a Patch W of the SMQT features M(x), then a classfer non hx M x hx M x (4) xw xw non Can be acheved usng the non table h x, the table h x and defnng a threshold for θ. Snce both tables work on the same doman, ths mples that one sngle lookup-table h h h (5) non x x x can be created for sngle lookup-table classfcaton. The tranng database contan 1, 2,, N feature patches wth the SMQT features (x) and the correspondng classes c ( or non ). The non table and the table can then be traned wth the Wnnow Update Rule. Intally both tables contan zeros. If an ndex n the table s addressed for the frst tme durng tranng, the value (weght) on that ndex s set to one. There are three tranng parameters; the threshold γ, the promoton parameter α > 1 and the demoton parameter 0< β < 1. If hx M x and c s a then promo- xw ton s conducted and s a then promoton s conducted as follows hx M x hx M xxw (6) If c s a non and hx M x then demoton takes place xw h M x h M x x W (7) x x
4 522 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features Ths procedure s repeated untl no changes occur. Tranng of the non table s performed n the same manner, and fnally the sngle table s created accordng to Equaton (5). One way to speed up the classfcaton n object recognton s to create a cascade of classfers [15]. Here the full SNoW classfer wll be splt up n sub classfers to acheve ths goal. Note that there wll be no addtonal tranng of sub classfers nstead the full classfer wll be dvded. Consder all possble feature combnatons for one feature, P, 1,2,, 2L D, then 2LD 1 vx hx P, x W results n a relevance value wth respectve sgnfcance to all features n the feature patch. Sortng all the feature relevance values n the patch wll result n an mportance lst. Let W W be a subset chosen to contan the features wth the largest relevance values. Then xw x (8) h M x (9) can functon as a weak classfer, rejectng no s wthn the tranng database, but at the cost of an ncreased number of false detectons. The desred threshold used on θ' s found from the n the tranng database that results n the lowest classfcaton value from Equaton (9). Extendng the number of sub classfers can be acheved by selectng more subsets and performng the same operatons as descrbed for one sub classfer. Consder any dvson, accordng to the relevance values, of the full set WW W. Then W' has fewer features and more false detectons compared to W'' and so forth n the same manner untl the full classfer s reached. One of the advantages of ths dvson s that W'' wll use the sum result from W'. Hence, the maxmum of summatons and lookups n the table wll be the number of features n the patch W. 6. Face Detecton Tranng and Classfcaton The detector analyzes mage patches pxels s appled. Ths patch s extracted and classfed by jumpng Δx = 1and Δy = 1 pxels through the whole mage. In order to fnd s of varous szes, the mage s repeatedly downscaled and reszed wth a scale factor Sc = 1.2. To overcome the llumnaton and sensor problem, the proposed local SMQT features are extracted. Each pxel wll get one feature vector by analyzng ts vcnty. Ths feature vector can further be recalculated to an ndex m V x L (10) where V(x ) s a value from the feature vector at poston. Ths feature ndex can be calculated for all pxels whch results n the feature ndces mage. A crcular mask contanng P = 648 pxels s appled to each patch to remove background pxels, avod edge effects from possble flterng and to avod undefned pxels at rotaton operaton. The and non tables are traned wth the parameters α = 1.005, β = and γ = 200. The two traned tables are then combned nto one table accordng to Equaton (5). Gven the SNoW classfer table, the proposed splt up SNoW classfer s created. The splts are here performed on 20, 50, 100, 200 and 648 summatons. Ths settng wll remove over 90% of the background patches n the ntal stages from vdeo frames recorded n an offce envronment. Overlapped detectons are pruned usng geometrcal locaton and classfcaton scores. Each detecton s tested aganst all other detectons. If one of the area overlap ratos s over a fxed threshold, then the dfferent detectons are consdered to belong to the same. Gven that two detectons overlap each other, the detecton wth the hghest classfcaton score s kept and the other one s removed. Ths procedure s repeated untl no more overlappng detectons are found. 7. Expermental Dscusson & Results Our experments are performed usng Matlab ver. 7.4, CPU 2.13GHZ to verfy the effectveness of the proposed method. The proposed method s appled on 150 color mages gathered from varous sources such as Internet, UCD Face Image Database and Georga Database. These mages are varyng n: sze, lghtng effects, unform and nonunform background, number of person n each mage and the rotaton angle of person. Fgure 3 shows some of the output of tested mages n Fgure 4 obtaned by applyng proposed method and SFSC method. As can been seen n Fgure 3 the detecton performance of the proposed method s better than SFSC method. Fgure 5 llustrates Comparson between proposed method and SFSC method n terms of detecton rate, false postve rate and false negatve rate. As can be seen n Fgure 5, detecton rate n proposed method s better than SFSC method. The proposed method could detect approxmately 84.1% of the s correctly and SFSC method could detect approxmately 74.6% of the s correctly. Although false postve rate and false negatve rate n proposed method s less than n SFSC method. In proposed method false postve rate s 10.4% and false negatve rate s 15.9%. In SFSC method false postve rate s 22.0% and false negatve rate s 25.4%. Fgure 6 llustrates detecton tme among 150 mages n comparson of proposed method and SFSC method, as can be seen detecton tme n proposed method s a lttle bt ncreased.
5 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features SFSC method 523 Proposed method Fgure 3. Detected s after applyng SFSC method and proposed method.
6 524 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features Detecton Tme Image Number SFSC proposed Fgure 6. Detecton tme of the two methods. Is099p5sn fortosearch,com Is fotosearch.com 8. Concluson In ths paper, we presented a new approach for detecton usng the MMCbCr Color Correcton approach and SFSC detecton method. The whole experment s appled on 150 color mages obtaned from dfferent sources from Internet, and Georga Database. Usng matlab 7.4, the expermental results show that the proposed method s more effectve and accurate compared to SFSC detecton method. Rate (Percent) Fgure 4. Samples of test mages. Detecton Rate False postve rate False negatve rate Fgure 5. Comparson of two methods. Proposed SFSC REFERENCES [1] Y. Mng-Hsuan, K. Davd and A. Narendra, Detectng Faces n Images: A Survey, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 24, No. 1, 2002, pp [2] I. Km, J. Hyung Shm and J. Yang, Face Detecton, Stanford Unversty, eports/ee368group02.pdf [3] M. A. El-Sayed and N. Aboelwafa, Study of Face Recognton Approach Based on Smlarty Measures, Internatonal Journal of Computer Scence Issues (IJCSI), Vol. 9, No. 2, 2012, pp [4] M. A. El-Sayed and M. A. Khafagy, An Identfcaton System Usng Eye Detecton Based on Wavelets and Neural Networks, Internatonal Journal of Computer and Informaton Technology, Vol. 1, No. 2, 2012, pp [5] M. A. El-Sayed, Edges Detecton Based on Reny Entropy wth Splt/Merge, Computer Engneerng and Intellgent Systems (CEIS), Vol. 3, No. 9, 2012, pp [6] M. Nlsson, J. Nordberg and I. Claesson, Face Detecton Usng Local SMQT Features and Splt up SNOW Classfer, IEEE Internatonal conference on Acoustcs, Speech, and Sgnal Processng (ICASSP), Vol. 2, 2007, pp [7] W. Kenzle, G. Bakr, M. Franz and B. Schölkopf, Face Detecton Effcent and Rank Defcent, In: Y. Wess, Ed., Advances n Neural Informaton Processng Systems,
7 Enhanced Face Detecton Technque Based on Color Correcton Approach and SMQT Features 525 Vol. 17, MIT Press, Cambrdge, 2005, pp [8] Z. Shaaban, Face Detecton Methods, World Scentfc and Engneerng Academy and Socety (WSEAS), [9] J. Wu and Z.-H. Zhou, Effcent Face Canddates Selector for Face Detecton, Pattern Recognton, Vol. 36, No. 5, 2003, pp [10] H. A. Rowley, S. Baluja and T. Kanade, Neural Network-Based Face Detecton, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 20, No. 1, 1998, pp [11] J. Yn and J. R. Cooperstock, Color Correcton Methods wth Applcatons to Dgtal Projecton Envronments, Journal of WSCG, 2004, n press. [12] M. A. Berbar, Novel Colors Correcton Approaches for Natural Scenes and Skn Detecton Technques, Interna- tonal Journal of Vdeo & Image Processng and Network Securty IJVIPNS-IJENS, Vol. 11, No. 2, 2011, pp [13] E. Prathbha, A. Manjunath and R Lktha, RGB to YCbCr Color Converson Usng VHDL Approach, Internatonal Journal of Engneerng Research and Development, Vol. 1, No. 3, 2012, pp [14] B. Froba and A. Ernst, Face Detecton wth the Modfed Census Transform, 6th IEEE Internatonal Conference on Automatc Face and Gesture Recognton, Seoul, May 2004, pp [15] P. Vola and M. Jones, Rapd Object Detecton Usng a Boosted Cascade of Smple Features, Proceedngs of the 2001 IEEE Computer Socety Conference on Computer Vson and Pattern Recognton (CVPR), Vol. 1, 2001, pp
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