50120130405034

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50120130405034

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 5, September – October (2013), pp. 292-299 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME REVIEW ON PALMPRINT RECOGNITION METHODS Ms. Sneha Vivek Sonawane Department of Computer Engineering MPSTME, SVKM’s NMIMS, Shirpur, Maharashtra, India Dr. M. V. Deshpande Department of Computer Engineering MPSTME, SVKM’s NMIMS, Shirpur, Maharashtra, India Ms. Arundhati Sahoo Department of Computer Engineering MPSTME, SVKM’s NMIMS, Shirpur, Maharashtra, India ABSTRACT Palmprint based recognition is becoming very popular now a days as palmprint provides features like principal lines, minutiae features, ridge orientation and creases. These features are very helpful for verification or the identification of an individual. Most of the research has been done in palmprint recognition due to its stability, reliability and uniqueness. Moreover it has been used for law enforcement, civil applications and access control applications. Researchers have proposed a variety of palmprint preprocessing, feature extraction and matching approaches. This paper provides a review of palmprint recognition approaches and analysis of methods. Keywords: Biometrics, Palmprint Recognition, Palmprint Features. I. INTRODUCTION Biometrics system refers to the identification of an individual by means of their unique physiological and behavioral characteristics [11]. Biometrics system is basically used for identification purpose and also for security purpose like access control. There are different types of modalities available for identification purpose such as iris, fingerprints, palmprint, face etc [13], where iris and fingerprint modalities are widely used in biometrics system as these two modalities are most reliable and possess uniqueness. Palmprint is also one of the reliable modality since it possess 292
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME more features than that of the other modality such as principal lines, orientation, minutiae, singular points etc. Also palmprint modality is unique for each individual [13], moreover it is universal. Palmprint recognition is used in civil applications, law enforcement and many such applications where access control is essential. Palm has features like geometric features, delta point’s features, principal lines features, minutiae, ridges and creases [13]. Principal lines are namely heart line, head line and life line [12]. Figure 1 shows structure of palmprint. Palmprint contains three principal lines which divides palm into three regions: Interdigital, Hypothenar and Thenar. An Interdigital region lies above the Heart line. The Thenar lies below the Life line. And Hypothenar is between Heart and Life line. From palmprint principal lines, minutiae, ridges features can be extracted for identification. Palmprint recognition techniques have been grouped into two main categories [8], first approach is based on low-resolution features and second approach is based on high-resolution features. First approach make use of low-resolution images (such as 75 or 150 ppi), where only principal lines, wrinkles, and texture are extracted [5]. Second approach uses high resolution images (such as 450 or 500 ppi) [1] [8], where in addition to principal lines and wrinkles, more discriminant features like ridges, singular points, and minutiae can be extracted. Figure 1. Structure of palmprint (principal lines, ridges, creases and minutiae in a palmprint) The rest of this paper is organized as follows: In Section II the architecture of palmpriint recognition system is introduced. Section III describes the related work of palmprint recognition. Discussion and analysis is made in section IV. Finally, we make conclusions in Section V. II. ARCHITECTURE OF PALMPRINT RECOGNITION Palmprint based recognition can operate in either identification or verification mode. Palmprint identification refers to one-to-many match, means input palmprint of an individual is matched with all templates present in database. It conform the identity of an individual. Palmprint verification refers to one-to-one match, means input palmprint of an individual is matched with its own templates present in database for which an individual claims [11]. Palmprint recognition system basically follows four steps that are image acquisition, preprocessing, feature extraction and matching. Figure 2 shows general block diagram of palmprint recognition system. 293
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Figure 2. General block diagram of palmprint recognition system Image Acquisition In this phase, image of palmprint is first capture with the help of different types of digital cameras. Acquired image may be blurred or it may have noise, which decreases the quality of an image and affects the performance rate of palmprint recognition system directly. The plamprint image acquired may vary by position, direction and stretching degree [13]. A. Pre-processing After capturing the data or image of the palmprint, preprocessing is formed on image. Sometimes noise is present in the captured image, noise can be remove with help of filters in processing phase. Images need to be normalized; this is also done in preprocessing phase. This phase need not necessary to accomplish. B. Feature Extraction Feature extraction is followed by pre-processing. In feature extraction phase features of palm are extracted like principal lines, orientation field, minutiae, density map, texture, singular points etc. These features are helpful for identification or verification of individual. Extracted features are stored in database for further process of matching. C. Matching Matching is next to the feature extraction phase. Feature matching determines the degree of similarity of recognition template with master template [13]. Different approaches are used for matching. Input provided by individual is matched with templates present in database. Matching is dependent on whether the system performs identification or verification. If it performs identification then one-to-many matching, which matches input as palmprint of individual with all templates of database otherwise one-to-one match is done for verification, where input of an individual is matched with only the template he/she claims to be. D. III. RELATED WORK In the last few years, a lot of research has been done in the palmprint recognition area, where researchers have proposed many methods for palmprint recognition. G. Lu, D. Zhang and K. Wang [14] has proposed palmprint recognition using eigenpalms features, where original image of 294
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME palmprint is transformed into the feature space i.e. eigenpalm with the help of K-L transform. Euclidean distances classifier is used for matching of recognition template and master template. Cappelli, Ferrara, and Maio [1] proposed high resolution palmprint recognition system which is based on minutiae extraction. Pre-processing is formed by segmentation of an image from its background. To enhance the quality of image, local frequencies and local orientations are estimated. Local orientation is estimated using fingerprint orientation extraction approach and local frequencies are estimated by counting the number of pixels between two consecutive peaks of gray level along the direction normal to local ridge orientation. Minutiae feature is extracted in feature extraction phase. To extract the minutiae features contextual filtering with Gabor filters approach is applied. Minutiae cylinder code has been used for matching the minutiae features. Kong, Zhang and Li [2] have proposed palmprint verification using 2-DGabor filter. 2-D Gabor filters for feature extraction from palmprint. In the pre-processing of an image low pass filter and boundary tracking algorithm is applied. Texture feature is extracted using the texture-based feature extraction technique which uses the 2-D Gabor Filter. Palmprint matching is based on normalized hamming distance. Huanga, Jiaa and Zhang [3] have proposed the palmprint verification system based on principal line extraction. Modified finite Radon transform has been used for feature extraction. A feature considered is principal lines. For matching of test image with a training image the line matching technique has been used which is based on pixel-to-area algorithm. Zhang, Kong, You and Wong [4] have proposed Online Palmprint Identification. The proposed system takes online palmprints, and uses low resolution images. Low pass filter and boundary tracking algorithm is used in pre-processing phase. Circular Gabor filter used for feature extraction and 2-D Gabor phase coding is used for feature representation. A normalized hamming distance is applied for matching. Konga, Zhanga, and Kamel [5] have proposed palmprint identification using feature level fusion. Multiple elliptical Gabor filters with different orientations are used to extract the phase information. Phase information is then merged according to a fusion rule to produce a single feature called the Fusion Code. Matching of two Fusion Codes is measured by normalized hamming distance. Jiaa, Huanga and Zhang [6] have proposed palmprint verification based on robust line orientation code. Modified finite Radon transform has been used for feature extraction, which extracts orientation feature. For matching of test image with a training image the line matching technique has been used which is based on pixel-to-area algorithm. Prasad, Govindan and Sathidevi [7] have proposed Palmprint Authentication Using Fusion of Wavelet Based Representations. Features extracted are Texture feature and line features. In proposed system pre-processing includes low pass filtering, segmentation, location of invariant points, and alignment and extraction of ROI. OWE used for feature extraction. The match scores are generated for texture and line features individually and in combined modes. Weighted sum rule and product rule is used for score level matching. Dai and Zhou [8] introduces high resolution approach for palmprint recognition with multiple features extraction. Features like minutiae, density, orientation, and principal lines are taken for feature extraction. For orientation estimation the DFT and Radon-Transform-Based Orientation Estimation are used. For minutiae extraction Gabor filter is used for ridges enhancement according to the local ridge direction and density. Density map is calculated by using the composite algorithm, Gabor filter, Hough transform. And to extract the principal line features Hough transform is applied. SVM is used as the fusion method for the verification system and the proposed heuristic rule for the identification system. As the problem of distortion due to many creases on palmprint and discrimination power of different regions of palmprint Dai, Feng, Zhou [10] introduces segment-based palmprint matching 295
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME and fusion algorithm, where whole palmprint image is divided into different regions and then each region is separately matched to deal with distortion. The similarity of two palmprints is calculated by fusing the similarity scores of different segments using a Bayesian framework. Dai, Feng, Zhou [10] also an orientation field-based registration algorithm is used to reduce the computational complexity. P.S. Sanjekar [16] used haar wavelet for palmprint identification which is image based method. For matching difference vector formula is used and features extracted are standard deviation and mean. IV.DISSCUSION AND ANALYSIS Researchers have introduced different methods for palmprint recognition. The analysis of time required to execute these methods, FRR and ERR is done in this section. G. Lu, D. Zhang and K. Wang [14] used 3096 sample images of palmprints and the feature recognition rate achieved is 99.19%. The PolyU Palmprint Database contains 7752 grayscale images of 386 different palms. The resolution of all the original palmprint images is 384 × 284 pixels at 75 dpi [6]. Time required for pre-processing, feature extraction and matching is 316ms, 70ms, 3.9ms respectively. Total execution time is 389.9 ms. Hong Kong Polytechnic University (PolyU) Palmprint Database contains 7752 gray scale images, corresponding to 386 different palms. The resolution of all the original palmprint images is 384 × 284 pixels and75 dpi images of user are used. The total execution time required is about 0.7 s. Gabor filter cannot correctly detect the directional energies of wide lines in image but MFRAT is basically used to detect directional energies of wide lines therefore, MFRAT is used by [3]. Palmprint images from 193 individuals are captured and 75 dpi resolution images of user are used [4]. The execution time for the pre-processing, feature extraction and matching are 538 ms, 84 ms, and 1.7 ms, respectively. The total execution time is about 0.6 seconds. Palmprint images from 284 individuals are capture, resulting in a total number of 11,074 images from 568 different palms. The size of images used is 384 × 284 with a resolution of 75 dpi [5]. Time required for preprocessing, feature extraction and for matching is 267 ms, 123 ms, 18ms respectively. The total identification time is 0.41 s and for verification 0.39 s is requires. PolyU-online-palmprint-database is used [7]. This database contains low resolution palmprints, these are 75 dpi resolution. The equal error rate obtained is 1.37%. Thus, fusion of line and texture features can reduce the EER significantly by 39.38% at minimal computational burden. Tsinghua Palmprint Database (THUPALMLAB) is used [1]. It contains 1280 palmprint images from 80 different subjects (left and right palms of each subject, eight impressions per palm). Images were captured using a palmprint scanner from Hisign; the image size is 2040 × 2040 pixels, the resolution 500 dpi which are high resolution images. Equal Error Rate (ERR) achieved is less than 0.01. Average time required to extract the all features is 1.935 s and 0.038s are required for matching. Minutiae Cylinder-Code is efficient against skin distortion; therefore it gives more accurate result than other matching techniques. But the proposed algorithm takes more time to minutiae extraction and orientation extraction. Dai and Zhou [8] uses THUPALMLAB for palmprint recognition. Image used for recognition is of size 2040 × 2040 pixels with 500 ppi resolution. False rejection rate for verification is 16% and achieved identification partial palmprint recognition rate is 91.7%. Wai Kin Kong, David Zhang, W. Li [2] database contains 4647 palmprint images collected from 120 individuals. Images captured are of two resolutions: 384×284 and 768×568 and 75 ppi images of user are used these are very low resolution images which directly affects the performance.. The texture features gives accuracy of FRR or FAR lesser than that of the minutiae, ridges or principal line features. Palmprint can be combining with other modality to increase the performance of [15]. 296
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Table 1 show the analysis of palmprint recognition where different methods for feature extraction and for matching are used by authors. Also it shows that, which features are extracted for matching at the time of feature extraction and analysis of time required for execution, EER, FRR. Table -1 Literature Analysis Author Features Extracted G. Lu, D. Zhang and K. Wang [14] Eigenpalm Jiaa, Huangaand, Zhang [6] Huanga, Jiaa, Zhang [3] Principal Lines Zhang, Kong, You, Wong [4] Feature Extraction Method K-L Transform Matching Method Remark Euclidean Distance Classifier Feature Recognition rate 99.19% , Total execution time is 389.9 ms Total execution time is about 0.7 s The total execution time is about 0.6 s Total identification time is 0.41 s and for verification 0.39 s is requires. EER is 1.37%. Modified finite Radon transform Modified finite Radon transform Line matching technique Line matching technique Texture features Circular Gabor filter Normalized hamming distance Konga, Zhanga, Kamel [5] Phase information or Fusion Code Multiple elliptical Gabor filters Normalized hamming distance Prasad, Govindan, Sathidevi [7] Texture feature and principal line OWE Cappelli, Ferrara, Maio [1] Minutiae Contextual filtering with Gabor filters Weighted sum rule and product rule Minutiae cylinder code Dai and Zhou [8] minutiae, density, orientation, and principal lines Composite algorithm , Gabor filter, Hough transform Principal Lines Support Vector Machine, heuristic rule ERR less than 0.01 and Total execution time required is 1.973s FRR for verification is 16% and for identification partial palmprint recognition rate is 91.7%. V. CONCLUSION Summarizing we can say that palmprint recognition is highly reliable modality than other one as it possesses choice of features like minutiae, principal lines, density map, ridges, and creases, delta point. Palmprint is unique for all the users. Palmprint recognition has various phases as image preprocessing, feature extraction and matching. Researchers worked on low resolution images of palmprint. A low resolution image directly affects the recognition rate of user as low resolution images unable to provide the most reliable feature like minutiae. High resolution images provide 297
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME more discriminent features which affect the recognition rate of user. Extracting more than one feature makes palmprint recognition reliable and also increases the recognition rate. The future work can be extended to apply gaussianization, the feature normalization method on the high resolution images where multiple features can be extracted. Principal component analysis PCA based gaussianization normalizes the individual components of the extracted feature vectors of user so the normalized vectors are better suited for classification. Dimensional feature vector transforms the feature components in such a way that, their distribution approximates the normal distribution with a predefined mean and standard deviation. Feature normalization on high resolution images reduce the time required for matching the extracted features of user with the master template and also reduce the error rate of FAR, FRR. VI. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] R. Cappelli, M. Ferrara, and D. Maio, “A Fast and Accurate Palmprint Recognition System Based on Minutiae,” IEEE Transaction on System, Man and Cybernetics- Part B: Cybernetics, Vol. 42, No. 3, pp. 1083-4419, June 2012. W. K. Kong, D. Zhang, W. Li, “Palmprint feature extraction using 2-D Gabor Filters,” Pattern Recognition, Elesvier, pp. 2339 – 2347, 2003. Huang,W. Jia, D. Zhang, “Palmprint verification based on principal lines,” Pattern Recognition, Science Direct, pp.1316 – 1328, 2008. D. Zhang, W. K. Kong, J. You, M. Wong, “Online Palmprint Identification,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.25, No. 9, pp. 0162-8828, Sept 2003. A. Konga, D. Zhang, M. Kamel, “Palmprint identification using feature-level fusion,” Pattern Recognition, Science Direct, pp. 478 – 487, 2006. Huang,W. Jia, D. Zhang, “Palmprint verification based on robust line orientation code,” Pattern Recognition, Science Direct, pp. 1504 – 1513, 2008. S. M. Prasad, V. K. Govindan , P. S. Sathidevi, “Palmprint Authentication Using Fusion of Wavelet Based Representations,” IEEE, pp. 978-1-4244-5612-3, 2009. J. Dai and J. Zhou, “Multifeature-Based High-Resolution Palmprint Recognition,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.33, No. 5, pp. 0162-8828, May 2011. A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,” Pattern Recognition, Elesvier, vol. 42, no. 7, pp. 1408–1418, July 2009. J. Dai, J. Feng, J. Zhou, “Robust and Efficient Ridge-Based Palmprint Matching,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.34, No. 8, pp. 0162-8828, August 2012. http://www.cse.iitk.ac.in/users/biometrics/pages/what_is_biom_more.htm X.Q. Wu, D. Zhang, K.Q. Wang, B. Huang, “Palmprint classification using principal lines,” Pattern Recognition, Science Direct, pp. 1987–1998, 2004. A. Jain, P. Flynn, and A. Ross, Handbook of Biometrics. Springer, 2007. G. Lu, D. Zhang and K. Wang, “Palmprint recognition using eigenpalms features,” Pattern Recognition, Science Direct, pp. 1987–1998, 2003. P.S. Sanjekar, J.B. Patl, “An Overview of Multimodal Biometrics,” International Journal of Signal and Image Processing, Vol.4, No. 1, Feb 2013. P.S. Sanjekar, “Palmprint Identification by Wavelet Transform,” Proc. Of international conf. on Image Processing and vision System, Vol 1, Oct 2011. 298
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME AUTHOR’S PROFILE Ms. Sneha Vivek Sonawane is pursuing M.Tech at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, Dist. Dhule (Maharashtra) of SVKM’s NMIMS (Deemed to be University). Dr. Manojkumar Deshpande is Professor & Associate Dean of Mukesh Patel School of Technology Management and Engineering, Shirpur Campus, Dist. Dhule (Maharashtra) of SVKM’s NMIMS (Deemed to be University). He has published research papers related to Software Engineering, Intelligent Systems, Software Estimation, Soft Computing and Multimedia Systems. He is guiding post graduate students for projects. He has been listed as a guide for Ph.D. in Computer Engineering at SVKM’s NMIMS. Ms. Arundhati Sahoo is Assistant Professor at Mukesh Patel School of Technology Management and Engineering , Shirpur Campus, Dist. Dhule (Maharashtra) of SVKM’s NMIMS ( Deemed to be University).She is acting as a Co-guide for Post graduate students for projects. She has published research papers related to Image Processing Fabric Defect Detection by Moment feature, DCT and DFT method. 299

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