Image Matching Algorithm based on Feature-point and DAISY Descriptor

Transcription

1 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE Image Matchng Algorthm based on Feature-pont and DAISY Descrptor L L School of Busness, Schuan Agrcultural Unversty, Schuan Dujanyan , Chna Abstract Image matchng technology s the research foundaton of many computer vson problems, and the matchng algorthm based on partal features of mages s a research focus n ths feld. In order to overcome the unstable performance of classc SURF algorthm on rotaton nvarance, an mage matchng algorthm combned wth SURF feature-pont and DAISY descrptor s proposed. Based on the feature pont detecton of SURF algorthm, a prncpal drecton dstrbuton method for DAISY descrptor s put forward, and a novel DAISY descrptor s obtaned accordng to the rotaton of the prncpal drecton. In ths paper, our proposed algorthm, on the bass of slght ncrease n runnng tme, mproves the mage matchng capablty of the classc SURF algorthm on mage rotaton. The expermental results show that our proposed algorthm has stronger robustness n a varety of complex cases, such as mage blurrng, llumnaton varaton, JPEG compresson rato varaton, feld of vew varaton, etc. Our proposed algorthm can not only keep the merts of the orgnal SURF algorthm on computaton speed, but also mprove the matchng accuracy on rotaton nvarance. Index Terms Image Matchng; DAISY Descrptor; SURF Feature-Pont; Rotatonal Invarance I. INTRODUCTION Image matchng technology s the research foundaton of computer vson problems, such as mage regstraton, object recognton and trackng, 3D reconstructon, etc. It also s wdely used n such felds as remote sensng, medcne, artfcal ntellgence, etc. [1-5]. The essence of mage matchng s to determne the geometrcal transformaton relatonshp between the reference mage and the matched mage. Image matchng algorthms can be dvded nto two categores: gray-based matchng and feature-based matchng. The gray-based matchng algorthm s ntutve and takes full advantage of the gray scale nformaton, whle the dsadvantage of the algorthm s that t s senstve to nose and llumnaton varaton and ts calculaton stablty s not hgh. The feature-based matchng algorthm s currently stll research focus, where the most mportant steps are the mage feature extracton and matchng. In addton, the key of exactng and matchng s to obtan some feature-ponts wth the hgher correct matchng rate. Local feature descrptor n mage s a core step of the mage feature extracton and matchng process. Over the past decade, lots of scholars have done tons of researches on the local feature descrptor, where local nvarant feature descrptor s developed most quckly n computer applcaton feld [6 7]. It frst calculates the local feature descrptors for each local feature pont, and then determnes whether these feature-ponts can be matched, accordng to the dfferent descrptors. SIFT has proved to be the most robust local nvarant feature descrptor n object recognton and matchng. In the exstng descrptor, SIFT algorthm proposed by Lowe, has average optmal performance [8], but the computatonal of SIFT descrptor s very complexty, ts operaton s also s very tme-consumng. Therefore, on the bass of the SIFT algorthm, the SURF algorthm proposed by Bay et al. has been superor to the tradtonal SIFT algorthm [9]. It s worth notng that, although the SURF algorthm mproves on speed for 3-4 tmes faster than SIFT algorthm, some scholars dscover SURF algorthm s poor performance on a rotatonal nvarance when they are compared wth the performance of the local feature operator [10]. Therefore, f these local feature descrptors are ntroduced to overcome ths defcency, t wll have a very sgnfcant nfluence on the accuracy of the extracted feature-ponts and related follow-up work. DAISY descrptor [11] proposed by the Engn Tola et al. s a local nvarant feature descrptor whch s used n dense stereo matchng. In addton, ts matchng performance and operaton speed are relatvely good. Although DAISY descrptor does not have rotatonal nvarance, the calculaton of descrptor s very convenent because t has a central-symmetrcal structure, whch makes t s very easy to obtan rotaton nvarance. Stefan Leutenegger et al., who combned the DAISY descrptors wth BRIEF descrptor [12], have proposed novel BRISK descrptor [13], whch has the advantage of rapd convergence and good numercal stablty wth mnmum occupancy of computer storage. Based on the combnaton of features of the human retna wth DAISY descrptor, Alexandre Alah et al. have proposed a novel FREAK descrptor wth densty dstrbuton of the human retna [14], where the descrptor s a bnary descrptor and also has the advantage of rapd convergence. Yn Guo et al. have proposed an mproved DAISY descrptor algorthm [15]. Frstly, the prncpal drecton s assgned to DAISY descrptor, and then PCA s adopted to declne the dmensonalty of the descrptor. Fnally, combned wth Harrs corner detector, ths wll make the matchng more quckly. The algorthm has a good result n mage matchng test, but the dsadvantage s a large number of operatons n data processng, where the computaton do: /jmm

2 830 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE 2014 tme s close to SIFT algorthm. LIU Tan-lang, et al. have proposed a dense stereo matchng method based on DAISY descrptor and mproved weght kernel [16]. On the bass of these features, such as smple and low complcaton, the method has a hgher matchng accuracy. In a word, DAISY descrptor has a greater advantage than the prevous descrptors n the process of mage matchng, whch s more sutable for the mage matchng. In order to overcome the unstable performance of classc SURF algorthm on rotaton nvarance, a mage matchng algorthm combned wth SURF feature-ponts and DAISY descrptor s proposed. Based on feature pont detecton of the SURF algorthm, a prncpal drecton dstrbuton method for DAISY descrptors s put forward, and a novel DAISY descrptor s obtaned accordng to the rotaton of the prncpal drecton. In ths paper, our proposed algorthm, on the bass of slght ncrease n operaton cost, mproves the mage matchng capablty of the classc SURF algorthm on mage rotaton. Our proposed algorthm can not only keep the merts of the orgnal SURF algorthm n computaton speed, but also mprove the matchng accuracy on rotaton nvarance. II. SURF IMAGE MATCHING ALGORITHM BASED ON DAISY DESCRIPTOR SURF s a robust local feature detector, and can be used n computer vson tasks lke object recognton or 3D reconstructon. It s partly nspred by the SIFT descrptor. Therefore, smlarly to SIFT algorthm, the feature pont detecton of SURF algorthm stll s based on scale space theory. In contrast, SIFT algorthm adopts Dfference of Gaussans (DoG) to extract feature-ponts, whle SURF uses an nteger approxmaton as the determnant of Hessan blob detector, whch can be computed extremely quckly wth an ntegral mage. As for a pxel pont wth scale n mage, ts Hessan matrx can be denoted as Lxx ( X, ) Lxy ( X, ) H(X, )= (1) Lxy ( X, ) Lyy ( X, ) where L s the convoluton of the mage wth the second dervatve of the Gaussan. In order to speed up the computaton n SIFT algorthm, the Box flter s used to approxmately replace the Gaussan flter. In addton, SIFT algorthm smplfes the calculaton of determnant, whch no longer computes the weght of each regon separately, thus the determnant can be obtan by the followng Equaton: f f f det ( H)= ( ) (2) x y x y where f / x s the convoluton result of the mage wth the template. Due to the use of ntegral mage and the Box flter, the sze of the flter s only changed n the scale-space constructed by the SURF algorthm, whle the mage sze s constant. In contrast, the fltered mage s contnued to be fltered n SIFT flterng algorthm. SURF algorthm scale space s dvded nto several orders, and each order comprses a number of layers. Generally speakng, the number of orders s set to four, each order has four layer scale mages, where the bottom mage of each order s j orgnal mage. The sze of the flter n each layer s 2, where s the order of the mage and j s the layer of the mage. For nstance, the flter szes of the frst-order mage are 9,15,21,27, respectvely. The dfferent of the sze s only 6 n the frst order, whle the dfferent n other order s 12, 24, and 48. The correspondng scale of each s mage s 2, where s s the sde length of the flter n the current mage. After the approxmaton of the Hessan matrx determnant s obtaned n each layer, the non-maxmum suppresson s performed n neghborhood. Therefore, the pont can be selected as a feature pont when only the value of the current pont s bgger (smaller) than the value of 26 ponts around the pxel. Because the Box flter s used to approxmately replace the Gaussan flter and ntegral mage s used to accelerate the ntegraton process, t s possble to mprove the speed of operaton n the case of hgh accuracy. In order to make the SURF descrptor has rotaton nvarant, we frst need to determne the prncpal drecton of the feature ponts. Gven the scale sze of the feature pont s s xy, the Haar wavelet responses of the x and y drectons n the ntegral mage are computed n the regon wth a radus around the feature-pont, where the sze of Haar wavelet s h. In order to be more n lne wth the objectve actual stuaton, these responses are gven to Gaussan weghtng coeffcent, whch the closer the locaton s to the feature-pont, the stronger the weght, so the larger the correspondng contrbuton s. In other words, the farther the locaton s to the feature-pont, the smaller the correspondng contrbuton s. Then, the quantzaton step sze s set as 60 to calculate the sum of the response values of the Haar wavelet for each regon. Fnally, the maxmum dstrbuton response s selected as prncpal drecton of descrptor. After the prncpal drecton s selected, the axs s centered on the feature pont, and then s rotated to the prncpal drecton. Along wth the prncpal drecton, some rectangular areas around the feature ponts are selected to calculate the descrptor. The area s dvded nto sub-regons wth dfferent sze, and then the Haar wavelet response n each sub-area s calculated. The Haar wavelet response values of the horzontal drecton and the vertcal drecton relatves to prncpal drecton, whch are set to H x, H y, respectvely. It s not necessary to accumulate the response values n each drecton, but H xy s calculated so as to enhance the robustness of the algorthm. The nterest area s weghted wth a Gaussan Kernel at the nterest pont to gve some robustness for deformatons and translatons. As for each subarea of mage, the descrptor of an nterest pont s the 16 vectors. Fnally, the descrptor s normalzed so as to acheve varatons nvarance, whch can be represented as a lnear scale of the descrptor.

3 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE DAISY descrptor s a local mage descrptor, whch s very effcent to compute densely. Its core dea s to convolve the dfferent drectonal dagram of orgnal mage wth Gaussan flterng functon wth dfferent sze. Due to the separablty of Gaussan flterng functon, the method has hgh effcency, whch s often used n the process of stereo vson dense matchng. Fgure 1. DAISY descrptor constructon DAISY descrptor s smlar to dasy, whch s constructed by some central-symmetrcal crcles, as shown n Fgure 1. In general, around the red center pont, a concentrc structure of three layers wth dfferent radus s constructed, where there are 8 samplng ponts n each layer. These ponts are denoted wth blue sold dots, and dstrbute on 45 degree ntervals dstrbuton. Snce the samplng ponts per layer have the same Gauss value scale, the Gauss scale value gradually ncreases from the center to the outsde. Ths structure makes the DAISY descrptor has the better robustness for mage affne and llumnaton varaton [7]. In addton, unlke the SIFT algorthm and SURF algorthm that use rectangular neghborhood, DAISY descrptor uses the crcular neghborhood, whch s because the crcular neghborhood has the better postonng feature than rectangular neghborhood. And most of all, the DAISY descrptor can easly acheve the purpose of rotaton, so the DAISY descrptor s adopted to descrbe feature ponts. The basc flow of DAISY descrptor s constructed as follows: Frstly, the eght drecton gradent of a pxel on the orgnal mage can be represented as L D, where D xy denotes the gradent drecton. Then, the samplng pont Gauss convoluton value of each layer n concentrc crcles can be obtaned by multple Gauss convolutons. The Gauss scale values can be represented by Equaton (3), whch s the convoluton of the Gauss kernel wth the gradent mage. As for each pxel, a vector wth a length of 8 can be obtaned to represent local gradent drecton hstogram, whch s wrtten as H xy. Therefore, we can get the DAISY descrptor Equaton, whch s denoted as follows: xy xy 2 1 H H x = 2 x x (3) where, l denotes structural layers, H denotes the drecton of each layer, x denotes the coordnate of the samplng ponts on concentrc crcles centered around the pxel. Therefore, H s( x, y ) s local gradent drecton hstogram of the samplng pont. The structure n Fgure 1 has the better average performance on power consumpton, so ths paper also uses a smlar structure to verfy the algorthm and the obtaned feature vector also contans 8 dmensons. Eucldean dstance of two vectors s used to measure the smlarty between descrptors. Although orgnal DAISY descrptor does not have rotatonal nvarance, the calculaton of descrptor s very convenent because t has a central-symmetrcal structure, whch makes t s very easy to obtan rotaton nvarance [18]. Snce Gauss flter gradent drecton of orgnal mage s ndspensable when computng DAISY descrptor, the group of gradent drecton hstogram are smlar to the drecton hstogram of the SIFT algorthm. So the prncpal drecton dstrbuton method proposed n the lterature [18] s adopted to drectly calculate the gradent hstogram of the second layer of the center pont and the maxmum value drecton s took as the prncpal drecton of descrptor. The algorthm s equvalent to selectng drecton wth 45 degrees samplng, whch has fast computaton speed, but wll reduce the matchng accuracy. A novel prncpal drecton dstrbuton algorthm based on DAISY descrptor s proposed, whch wll mprove the correct matchng rate. We select sample ponts on the outsde crcle, where the angle of rotaton s denoted as the number of samplng ponts. Accordng to the obtaned gradent orentaton hstogram on the maxmum scale drecton, these ponts are accumulated so as to get the followng Equatons: 1 5 Dx(X )=I(X )-I(X ) (4) 3 7 Dy(X )=I(X )-I(X ) (5) Ths s a decson-functon to select the prncpal drecton. After the feature pont s calculated for a lap, 72 values wll be obtaned, and then the drecton of the bggest value s selected as the prncpal drecton of DAISY descrptor. The goal of choce s to use the gradent nformaton of each drecton as much as possble, whch has hgher precson than the prncpal orentaton dstrbuton algorthm of SURF algorthm and the algorthm of lterature [18]. After obtanng the DAISY prncpal drecton, the DAISY template wll be rotated and algned along the prncpal drecton. In addton, samplng ponts of template on the 24 concentrc crcles wll be recomputed accordng to the rotaton angle, and the new gradent hstogram s re-obtaned by the drecton map flterng wth the dfferent Gauss scale flter, so as to obtan new DAISY descrptor wth rotaton nvarant. The specfc flowchart of mage processng s descrbed as follows:

4 u,v S u 1 v u n v n, 1,, 832 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE ) Input the orgnal mage, compute ts ntegral mage, use Equaton (1) to compute Hessan matrx of the ntegral mage and non maxmum suppresson to detect feature pont. 2) Eght drecton gradent of the orgnal mage are computed, and then are fltered by Gauss flter. 3) The Equaton (3) s adopted to dstrbute prncpal orentaton for each feature pont, and then the Equaton (2) s used to get the feature descrptor after the DAISY descrptor s rotated to the prncpal drecton. 4) Nearest neghbor rato matchng strategy s adopted to match feature descrptors. Frst, compute Eucldean dstance of the descrptor feature vectors of feature pont between the reference mage and the matched mage. If the dstance of two feature ponts s the shortest, and the dstance s 0.7 tmes more than sub-shortest dstance, two feature ponts are consdered as matchng pont par; otherwse, we thnk the feature pont has not matchng pont. 5) Verfy the matchng pont-par. There are two methods to obtan the mappng transformaton matrx between the reference mage and the matched mage, whch are RANSAC algorthm and classc matchng database provded by Oxford Unversty as a real transformaton matrx. As for any a par of matchng ponts, these ponts are concded n the deal case after obtanng transformaton matrx. Due to the nfluence of the nose, two ponts cannot be completely concdent. Therefore, ths paper wll use symmetrc transfer error to judge, whch the threshold s set as When the symmetrcal transfer error cost functon value s less than the threshold, the matchng pont are the correct match pont; otherwse t s the error matchng pont. The specfc flowchart of mage processng s descrbed as follow n Fgure 2. Reference mage Feature pont detecton calculaton of Gauss Flter pattern III. dstrbuton DAISY formaton of DAISY descrptor mage matchng Fgure 2. Algorthm flowchart The matchng mage Feature pont detecton Calculaton of Gauss flter pattern dstrbuton DAISY the rotaton of the DAISY descrptor EXPERIMENT RESULT AND ANALYSIS A. Rotaton Invarance Detecton In order to verfy the poor performance of SURF algorthm on rotaton nvarance, some classcal mages are adopted as experment mages, and every mage s rotated from 0 to 180. For comparson, our proposed algorthm s compared wth SIFT algorthm, SURF algorthm, SURF prncpal orentaton + DAISY descrptor, and the algorthm proposed by lterature [18]. Precson and runtme of each matchng algorthm are recorded, where Matchng accuracy= Number of correct matchng pont / Total of matchng pont. The paper uses descrptor test method proposed by lterature [19 20] to evaluate the performance of algorthms, where SIFT and SURF descrptor use the parameter settngs of lterature [7] and lterature [8], respectvely. All of the experments are run under MATLAB v7.8 (R2012a) on PCs wth an Inter Xeon CPU at 3.2GHz and 2 GB memory. All processes are smulated n MATLAB envronment and do not nclude the MEX-fle. Matchng correct rate s shown n Fgure 3. Fgure 3. Matchng correct rate of mage Plane rotaton test TABLE I. PERFORMANCE COMPARISON OF ALGORITHMS IN ROTATION TESTS Images Algorthms AMCR MART ARTF SIFT 97.95% 32.54s 11.86ms SURF 78.79% 3.82s 2.52ms SURF+DAISY 90.36% 5.54s 3.65ms Plane Lterature [17] 81.17% 3.88s 2.56ms Our algorthm 95.78% 5.17s 3.40ms SIFT 94.68% 21.49s 12.99ms SURF 70.67% 3.47s 2.77ms SURF+DAISY 83.97% 5.19s 4.15ms Pepper Lterature [17] 69.24% 3.45s 2.75ms Our algorthm 88.75% 3.91s 3.13ms SIFT 99.17% 78.97s 10.6ms SURF 74.88% 5.76s 2.26ms SURF+DAISY 81.41% 8.24s 3.23ms Baboon Lterature [18] 72.05% 5.34s 2.09ms Our algorthm 88.83% 7.64s 2.99ms SIFT 95.8% 28.91s 11.52ms SURF 65.8% 3.41s 2.38ms SURF+DAISY 76.7% 5.11s 3.58ms Lena Lterature [18] 65.13% 3.36s 2.35ms Our algorthm 85.5% 4.45s 3.11ms Fgure 3 s the correct-rate curve dagram of fve matchng algorthms on Plane orgnal mage and rotaton mage wth the dfferent rotaton angle, where the x-coordnate and the y-coordnate ndcate rotaton angle and the matchng rate, respectvely. In addton, average performance of ths algorthm s shown n Table 1. By comparng these matchng rates from the Table 1, we can see that: SIFT descrptor has strong robustness on rotatonal nvarance, and s also the most stable wth the better result n all of algorthms. In addton, our

5 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE TABLE II. AVERAGE MATCHING CORRECT RATE OF ALGORITHMS ON EACH OXFORD CLASSIC MATCHING DATABASE (%) Algorthms Bkes Grafft Wall Leuven UBC Average SIFT SURF SURF+DAISY lterature [18] Our method TABLE III. AVERAGE RUNNING TIME OF ALGORITHMS ON EACH OXFORD CLASSIC MATCHING DATABASE (UNIT: SECOND) Algorthms Bkes Grafft Wall Leuven UBC Average SIFT SURF SURF+DAISY lterature [18] Our method proposed algorthm also has obtaned the most matchng ponts and average matchng accuracy can reach more than 95%. The performance of SURF algorthm has verfed ts poor performance on mage rotaton, average matchng accuracy can reach less than 70%, and the algorthm can obtan the less matchng ponts. The thrd algorthm uses drectly prncpal drecton of the SURF algorthm to calculate the DAISY descrptor and has consderable mprovement on average correct rate and the average number of correct matchng ponts than the SURF algorthm, whch shows DAISY descrptor matchng capablty s better than the orgnal SURF descrptor n the case of the same feature pont detecton and prncpal drecton. The fourth method s provded by the lterature [18], t uses drectly the center of the gradent hstogram n the second layer to dstrbute the prncpal drecton, whch s a better algorthm when the angle change s small or the multple of the angle. However, the rest of the angles wll cause the larger error on the matchng accuracy, whch s because the prncpal drecton s selected n unts of angel. The last one s our proposed algorthm. The average number of matchng ponts s very much close to the SIFT algorthm, the average accuracy rate s rased to near 90%, whch s shown that the prncpal drecton of our proposed algorthm s more sutable for DAISY descrptor than that of the SURF algorthm. In addton, our proposed algorthm s also more robust than the lterature [17]. By comparng the average operaton tme of sngle matchng(aotsm) and average computaton tme of sngle feature ponts (ACTSFP), we can see that SIFT algorthm s very tme-consumng, and the effcency of the algorthm s almost fve tmes as hgh as that of SURF algorthm. The lterature [17] s faster than the SURF algorthm, whch s because of ts smple structure and easy method for dstrbutng prncpal drecton. The two algorthms, namely, SURF prncpal drecton + DAISY descrptor, our algorthm), ther average runnng tmes are slghtly slower than the orgnal SURF algorthm, but our algorthm s slghtly faster than the prncpal drecton of the SURF algorthm, whch s shown our prncpal drecton dstrbuton method s faster than that of SURF algorthm. Consderng comprehensvely the matchng results and computaton tmes, our algorthm enhances the matchng capablty on the rotatonal nvarance n the case of a slght ncrease of runnng tme, and gets better results, whch shows that our algorthm s more superor to the orgnal SURF algorthm on the rotatonal nvarance. B. Matchng Results and Analyss on Classc Database The proposed descrptor s evaluated on the standard Oxford dataset, n whch mage pars are under varous mage transformatons, ncludng vewpont varaton, scale and rotaton varatons, mage blur, JPEG compresson and llumnaton varatons. Each group contans a total of sx real mages, the frst mage s denoted as a benchmark mage, and the rest of the mages are the matched mages. In addton, Homography Matrx between the frst mage and other mages s appended to data as the mappng transformaton matrx, so t s convenent to valdate the algorthm. It should be noted that the data wth a large-scale varaton s dscussed n ths paper, whch s because the DAISY descrptor dd not resolve the scale nvarance. Accordng to the above results, we can see that our algorthm has the best performance n the fuzzy mage (Bkes), whch s because the mage fuzzy has a bg effect on gradent hstogram of SIFT algorthm. DAISY descrptor tself uses the gradent hstogram fltered by Gauss flter as a feature descrpton, so mage blurrng has more promnent effect. Moreover, they are rotaton nvarant wthout relyng on a reference orentaton, further mprovng ther robustness. Snce SURF uses a smlar local feature n SURF mage matchng, the sgnfcant performance mprovement of our algorthm over SURF demonstrates the effectveness and advantage of our proposed feature poolng scheme,.e. poolng ntensty order s more nformatve than rngs. In most cases, our algorthm performance s better than DAISY. When mages have blur or llumnaton changes, our algorthm s better, especally when encounterng large llumnaton varaton. Snce the feld of vew alters, the matchng accuracy of all methods s substantally reduced, but the average correct rate s stll the hghest n the algorthm. Our algorthm s slghtly faster than the prncpal drecton of the SURF algorthm, whch shows prncpal drecton dstrbuton method s faster than that of SURF algorthm. In addton, our algorthm s sgnfcantly less than SIFT on runnng tme. Consderng

6 834 JOURNAL OF MULTIMEDIA, VOL. 9, NO. 6, JUNE 2014 comprehensvely the matchng results and computaton tme, our algorthm enhances the matchng capablty on the rotatonal nvarance n the case of a slght ncrease of runnng tme, and gets better results, whch shows that our algorthm s more superor to the orgnal SURF algorthm on the rotatonal nvarance and s also the most advantage. IV. CONCLUSION Based on the dsadvantage of orgnal SURF algorthm on rotaton nvarance, ths paper proposes a matchng algorthm combned wth SURF feature ponts and DAISY descrptor. The algorthm frst adopts the SURF Hessan matrx calculaton method to detect feature ponts so as to keep the quckness and accuracy of feature pont durng detecton process, then calculates the gradent drecton mage of the orgnal mage and uses our proposed algorthm for DAISY descrptor to compute the prncpal drecton of feature ponts. After the prncpal drecton s selected, the axs s centered on the feature pont, and then s rotated to the prncpal drecton. Along wth the prncpal drecton, some rectangular areas around the feature ponts are selected to calculate the descrptor. Expermental results show that our algorthm mproves the rotaton nvarance of orgnal SURF algorthm whle ncreasng the runnng tme slghtly, whch can obtan more correct matchng ponts. The proposed descrptor s evaluated on the standard Oxford dataset, n whch mage pars are under varous mage transformatons, ncludng vewpont varaton, scale and rotaton varatons, mage blur, JPEG compresson and llumnaton varaton. Our proposed mage matchng algorthm s combned wth SURF feature ponts and DAISY descrptor, whch has better runnng speed and stronger robustness than classcal algorthms. However, our algorthm s not very deal n the case of large mage scale varaton, whch wll be an mprovement drecton for the future work. REFERENCES [1] Wagner, Andrew, et al. "Toward a practcal face recognton system: Robust algnment and llumnaton by sparse representaton." Pattern Analyss and Machne Intellgence, IEEE Transactons on 34.2 (2012): [2] Rhodes, Matthew G., and Jeffrey S. Anastas. "The own-age bas n face recognton: a meta-analytc and theoretcal revew." Psychologcal bulletn138.1 (2012): 146. [3] De Heerng, Adélaïde, Bruno Rosson, and Daphne Maurer. "Developmental changes n face recognton durng chldhood: Evdence from uprght and nverted faces." Cogntve Development 27.1 (2012): [4] Gu, Je, et al. "Dscrmnant sparse neghborhood preservng embeddng for face recognton." Pattern Recognton 45.8 (2012): [5] Tanaka, James W., Bonne Heptonstall, and Smen Hagen. "Perceptual expertse and the plastcty of other-race face recognton." Vsual Cogntonahead-of-prnt (2013): [6] Zou, Wlman WW, and Pong C. Yuen. "Very low resoluton face recognton problem." Image Processng, IEEE Transactons on 21.1 (2012): [7] Zhong S, Xa K, Yn X, et al. The representaton and smulaton for reasonng about acton based on Colored Petr Net// Informaton Management and Engneerng (ICIME), 2010 The 2nd IEEE Internatonal Conference on. IEEE, 2010: [8] Le, D., Jn, Y., Xa, K., & Ba, G. (2010, March). Adaptve error control mechansm based on lnk layer frame mportance valuaton for wreless multmeda sensor networks. In Advanced Computer Control (ICACC), nd Internatonal Conference on (Vol. 1, pp ). IEEE. [9] Xa K, Ca J, Wu Y. Research on Improved Network Data Fault-Tolerant Transmsson Optmzaton Algorthm. Journal of Convergence Informaton Technology, 2012, 7(19). [10] XIA, K., WU, Y., REN, X., & JIN, Y. (2013). Research n Clusterng Algorthm for Dseases Analyss. Journal of Networks, 8(7), [11] Yao, Yufeng, Jny Chang, and Kajan Xa. "A case of parallel eeg data processng upon a beowulf cluster." Parallel and Dstrbuted Systems (ICPADS), th Internatonal Conference on. IEEE, [12] Ka-jan, Xa, et al. "An edge detecton mproved algorthm based on morphology and wavelet transform." Computer and Automaton Engneerng (ICCAE), 2010 The 2nd Internatonal Conference on. Vol. 1. IEEE, [13] Hofmann, Martn, et al. "Combned face and gat recognton usng alpha matte preprocessng." Bometrcs (ICB), th IAPR Internatonal Conference on. IEEE, [14] Connolly, Jean-Franços, Erc Granger, and Robert Sabourn. "An adaptve classfcaton system for vdeo-based face recognton." Informaton Scences192 (2012): [15] L, Annan, Shguang Shan, and Wen Gao. "Coupled bas varance tradeoff for cross-pose face recognton." Image Processng, IEEE Transactons on 21.1 (2012): [16] Sugura, Motoak, et al. "Self face recognton n socal context." Human bran mappng 33.6 (2012): [17] Mkc, Ivana, et al. "Movng shadow and object detecton n traffc scenes." Pattern Recognton, Proceedngs. 15th Internatonal Conference on. Vol. 1. IEEE, [18] Prat, Andrea, et al. "Detectng movng shadows: algorthms and evaluaton." Pattern Analyss and Machne Intellgence, IEEE Transactons on (2003):