Feature Extractions for Iris Recognition

Transcription

1 Feature Extractons for Irs Recognton Jnwook Go, Jan Jang, Yllbyung Lee, and Chulhee Lee Department of Electrcal and Electronc Engneerng, Yonse Unversty 134 Shnchon-Dong, Seodaemoon-Gu, Seoul, KOREA Emal: Tel: (82-2) Fax: (82-2) Abstract. In ths paper, we evaluate the performance of feature extracton methods for rs pattern classfcaton. Generally, the dentfcaton system usng rs recognton conssts of the rs localzaton block and the rs pattern classfcaton block. In ths paper, we used the 2D bsecton-based Hough transform and the radus hstogram method for the rs localzaton and we used multlayer perceptrons for the rs pattern classfcaton. Then, the three lnear feature extracton methods are evaluated to reduce the classfcaton tme and system complexty. The evaluated feature extracton methods are the feature extracton based on decson boundary, the canoncal analyss, and the prncpal component analyss. Experments wth 1831 rs mages show that the feature extracton based on decson boundary and the canoncal analyss show a favorable performance for the rs recognton. 1. Introducton Bometrcs s the dentfcaton and verfcaton method through the recognton of a person's bologcal trat and a wdely varety of methods based on the bometrc technology have been developed.e. fngerprnt, voceprnt, face, palms, rs, and retnal blood vessels. Among the methods, the human rs recognton s a relatvely new nonnvasve bometrc technology. The human rs pattern does not change wth age, cannot be forged, and s hghly dstnctve to an ndvdual, provdng a hgh level of securty and survellance [1, 2]. The personal dentfcaton and verfcaton system usng the rs encodes the rs pattern acqured by a camera and realzes personal dentfcaton by matchng the encoded rs features aganst the users rs features that were regstered n advance. For dentfcaton, the matchng process produces a decson whch s a record that has been ndexed relatve to a larger set of entres and the can be vewed as a pattern classfcaton problem. In ths paper, we use a multlayer perceptron for recognton of rs patterns and evaluate the varous lnear feature extracton methods for the multlayer perceptron.

2 2. Localzng Irs 2.1 Image Acquston An mage surroundng human eye regon s obtaned at a dstance from a CCD camera wthout any physcal contact to the devce. For fully automated systems for rs recognton, t s requred to mnmze person's nterventon n the mage acquston process. One smple way s to acqure a seres of mages wthn the specfc nterval and select the best one among them, but ths approach s strongly requred to have reasonable computatonal tme for real applcaton. In ths paper, we checked the mage qualty by consderng the pxel dstrbuton and the drectonal propertes of edge and the resultng napproprate mages were excluded from the next processng. Fg. 1 shows the examples of mages wth napproprate qualty. Fg. 1. Example of mages wth napproprate qualty. (a) the mage wth the blnk, (b) the mage whose the pupl part s not located n the mddle thus some parts of the rs area dsappear, (c) the mage obscured by eyelds or the shadow of eyelds, (d) the mage wth severe nose 2.2 Irs Localzaton The rs localzaton s to detect the area between pupl and sclera from an eye mage. In order to fnd out the rs area exactly, t s mportant to precsely detect the nner boundary between pupl and rs and the outer boundary between rs and sclera. At frst, we fnd the exact reference pont (.e. the center of the pupl) and then compute the dstance from the pont to the boundares as the radus. In ths paper, we propose a method to detect the reference pont and localze the rs area from an eye mage. At frst, the Canny edge detector s appled to the mage to extract edge components and then the connected components are labeled. Then, to get the center of the pupl, we use a 2D bsecton-based Hough transform, not a 2D gradent-based Hough transform [3]. The basc dea of the bsecton method s that any lne connectng two ponts of the crcle can be bsected by the lne whch passes through the center of the crcle. The frequency of each ntersectng pont among the perpendcular lnes formed by two ponts at a specfc dstance on the edge component s computed. The most frequently ntersected pont above a threshold ndcates the exstence of a crcle from the edge components, and the correspondng

3 pont can be consdered as the center of the crcle, the reference pont. Fg.2 shows each stage of bsecton-based Hough transform method. After detectng the canddate of the center, the radus hstogram s appled to valdate the exstence of a crcle and calculate ts radus. In order to compute the radus of the tentatve crcle, the nner boundary, we dvde the possble range of radus nto lots of sub-ranges, and select a sub-range wth the maxmal frequency and then determne the medan of the correspondng sub-range as the radus. By usng ths method, we can get the radus less senstve to nose. After determnng radus, we can easly fnd the nner boundary usng the center of the pupl and radus. For the outer boundary, the smlar process of gettng the nner boundary s appled. Fnally, the rs area can be localzed by separatng the part of an mage between the nner boundary and the outer boundary. Fg. 2. Each Stage of the bsecton-based Hough transform: (a) Orgnal mage (b) Edge detected mage (c) Plot of the frequency of the ntersecton ponts (d) Radus hstogram (e) Detected nner boundary (f) Detected outer boundary 2.3 Irs Encodng Multresoluton technques ntend to transform mages nto a representaton n whch both spatal and frequency nformaton are present [4]. In our scheme, all the rs mages are frst decomposed nto subbands usng wavelet transform. Wth the pyramd-structured wavelet transform, the orgnal mage s passed through low-pass and hgh-pass flters to generate the low-low, low-hgh, hgh-low and hgh-hgh submages. The decomposton s recursvely appled on the low frequency channel to obtan the lower resoluton submages. Thus, we can analyze an rs mage at both local and global scales smultaneously usng the multresoluton approach. Mallat's experments [4] suggest that by usng wavelet representaton, statstcs based on frst order dstrbuton of grey-levels mght be suffcent for pretentatve percepton of textual dfference. Hence we use four features.e. mean, varance, standard devaton, and energy from the grey-level hstogram of the subband mages.

4 In ths paper, each rs mage was decomposed nto three level usng Daubeches tap-4 flter whch resulted n 12 submages so as to extract rs features. We used the statstcal features to represent feature vectors, thus four statstcal features were computed from each subband mage. In addton to that, we dvde the sub mages nto local wndows n order to get robust feature sets aganst shft, translaton and nosy envronment (Fg. 3). We extracted statstcal features form local wndows on the correspondng submages, the submages of the ntermedate levels, to represent feature vectors. Fg. 3. Arrangement of feature vector by local wndows 3. Feature Extractons for Irs Recognton For smplcty and speed, the Hammng dstance or Eucldean dstance has been wdely used for rs pattern matchng. In ths paper, we use the multlayer perceptron for recognton of the rs pattern n order to compensate for the dstorton n the rs mage. And the three lnear feature extracton methods are evaluated to reduce the classfcaton tme and system complexty. In prncpal component analyss (PCA) [5], the egenvectors of the global covarance matrx are used as a new feature set. Although the PCA s optmal for sgnal representaton n the sense that the mean square error s mnmal for a gven number of features, generally t s not sutable for pattern classfcaton. The canoncal analyss (CA) [5] can be used to fnd the optmal features that maxmze a separablty crteron. In canoncal analyss (CA), wthn-class scatter matrx Σ w and between-class scatter matrx Σ b are defned and used to formulate a crteron functon. In the CA, feature vector d s selected to maxmze the followng functon: ~ T d Σ bd f ( d) = T d Σ d T where Σ w = Σ P(ω ) Σ, Σb = Σ P ( ω )( µ µ 0 )( µ µ 0 ), and µ = P ( ω ) µ. w 0 Σ Here, Σ, and P ω ) are the mean vector, the covarance matrx, and the pror µ ( probablty of class ω, respectvely. The CA provdes acceptable performances n most cases. However, snce the CA manly utlzes the mean dfferences between classes, the feature vector selected by the CA s not relable f mean vectors are near

5 to each other. Furthermore, CA does not work well for multmodal class problems, though neural networks are well suted for such problems. On the other hand, a feature extracton algorthm based on decson boundary has been proposed [6]. In decson boundary feature extracton (DBFE), t was shown that all the features necessary to acheve the same classfcaton accuracy as n the orgnal space can be obtaned from the vectors normal to decson boundares and the decson boundary feature matrx s used as a crteron functon as follows: Σ DBFM = K 1 T S N ( X) N ( X) p( X) dx where N(X) be the unt vector normal to the decson boundary at a pont X on the decson boundary p(x) s a probablty densty functon, S s the decson boundary, K = p(x) dx, and the ntegral s performed over the decson boundary. S It was shown that the rank of the decson boundary feature matrx s equal to the smallest dmenson where the same classfcaton could be obtaned as n the orgnal space, and the egenvectors of the decson boundary feature matrx of a pattern recognton problem correspondng to non-zero egenvalues are the necessary feature vectors to acheve the same classfcaton accuracy as n the orgnal space for the pattern recognton problem. The DBFE method has been successfully appled to multlayer perceptrons [7]. By extractng features drectly from decson boundares of the pattern classfer, t s possble to take a full advantage of multlayer perceptrons that can construct arbtrary decson boundares wthout assumng any underlyng probablty densty functon. Furthermore, the performance of the DBFE algorthm doesn t deterorate even f there s no mean dfference whle the CA fals n such a crcumstance. Although the DBFE algorthm for multlayer perceptrons showed a favorable performance, the vector normal to the decson boundary of a multlayer perceptron was estmated numercally, resultng n performance loss and a long computatonal tme. In order to address ths problem, recently, an analytcal verson of the DBFE algorthm for multlayer perceptrons has been proposed [8]. When the normal vectors were computed analytcally usng the analytcal decson boundary feature extracton (ADBFE), the processng tme for feature extracton was reduced and a notceable performance mprovement was acheved. 4. Experments and Results In order to evaluate the performance of the feature extracton algorthms for rs pattern recognton, tests were conducted usng real eye mages of 120 persons captured by dgtal camera. The mage sze used was and the total number of mage s The dmenson of feature vectors encoded by wavelet transform s 46. Two-layer perceptrons were used and the number of hdden neurons s the same as the dmenson of nput vector.

6 At frst, n order to evaluate the performance of the multlayer perceptron for rs recognton, we used the leave-one-out method, where all the samples except one sample s used for tranng and then the one sample s tested. We repeated the experment 1831 tmes and the fve samples were msclassfed. For comparson, the experments were conducted for the mnmum dstance classfer usng the Eucldean dstance as a dstance measure, resultng n msclassfcaton of the 31 samples. From these experments, t can be sad that multlayer perceptrons can mprove the performance of dentfcaton system usng rs. In the followng experment, we compare the performance of varous feature extracton algorthms. We used 10 randomly selected rs samples from each class for tranng and the rest for test. And the resultng total number of tranng mages s 1200 and rs feature vectors after feature extracton process are fed nto the multlayer perceptron for tranng and recognton. Fg. 4 shows the classfcaton accuracy for the 631 test data. Wth 46 features, the classfcaton accuracy of test data s 98%. Both ADBFE and CA show the favorable performance, achevng about the maxmum classfcaton accuracy wth 32 features. On the whole, the classfcaton accuraces whch the ADBFE method acheves are slghtly better than the classfcaton accuraces whch the CA method acheves. However, the PCA method always provdes the classfcaton accuraces lower than the other feature extracton methods, ndcatng that the PCA method s not sutable to feature extracton for pattern classfcaton Classfcaton Accuracy (%) ADBFE CA PCA Number of features

7 Fg. 4. Performance comparson of ADBFE(analytcal decson boundary feature extracton), CA(canoncal analyss), and PCA(prncpal component analyss) for the 631 test data. 5 Concluson The bometrc system usng the rs conssts of two man blocks: the rs localzaton and the rs classfcaton. In order to localze the rs from eye mages, we used the 2D bsecton-based Hough transform and the radus hstogram method. Then, the wavelet transform was used for encodng of rs mages and we extracted four features consstng of mean, varance, standard devaton, and energy from the greylevel hstogram of the subband mages. In ths paper, we used the multlayer perceptron for classfcaton of rs patterns and appled three feature extracton methods (analytcal decson boundary feature extracton, canoncal analyss, and prncpal component analyss) to the multlayer perceptron. The 1831 rs mages acqured from the 120 persons were used to evaluate the performance of the feature extracton methods. Expermental results show that the analytcal decson boundary feature extracton and the canoncal analyss show a favorable performance, but the prncpal component analyss s not sutable to feature extracton for rs pattern classfcaton. References 1. J. G. Daugman, Hgh Confdence Vsual Recognton of Persons by a Test of Statstcal Independence. IEEE Trans. on Pattern Analyss and Machne Intellgence, vol. 15, no. 11, , R. P. Wldes, Irs Recognton: An Emergng Bometrc. Proceedngs of IEEE, vol. 85, no. 9, , D. Ioammou, W. Huda, A. F. Lane, Crcle recognton through a 2D Hough transform and radus hstogrammng. Image and Vson Computng, vol. 17, 15-26, S. G. Mallet, A Theory for Multresoluton Sgnal Decomposton: The Wavelet Representaton. IEEE Trans. Pattern Recognton and Machne Intellgence, vol. 11, no. 4, , J. A. Rchards, Remote Sensng Dgtal Image Analyss: Sprnger-Verlag, C. Lee and D. A. Landgrebe, Feature extracton based on decson boundares. IEEE Trans. on Pattern Analyss and Machne Intellgence, vol. 15, , C. Lee and D. A. Landgrebe, Decson boundary feature extracton for neural networks. IEEE Trans. Neural Networks, vol. 8, 75-83, J. Go and C. Lee, Analytcal Decson Boundary Feature Extacton for Neural Networks. IEEE Int. Conf. on Geoscence and Remote Sensng, vol. 7, , 2000.