Abstract
In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) representation of palm images by two dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalized classification method based on Kernel Principal Component Analysis (Kernel PCA). Wavelet subband coefficients can effectively capture substantial palm features while keeping computational complexity low. We then kernel transforms to each possible training palm samples and then mapped the high-dimensional feature space back to input space. Weighted Euclidean linear distance based nearest neighbor classifier is finally employed for recognition. We carried out extensive experiments on PolyU Palmprint database includes 7752 palms from 386 different palms. Detailed comparisons with earlier published results are provided and our proposed method offers better recognition accuracy (99.654%).
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References
Zhan, D., Jing, X., Yang, J.: Biometric Image Discrimination Technologies. Computational Intelligence and Its Application Series, pp. 80–95. Idea Group Publishing, Hershey (2006)
Zhang, D., Shu, W.: Two Novel Characteristics in Palmprint Verification: Datum Point Invariance and Line Feature Matching. Pattern Recognition 32, 691–702 (1999)
Wu, X., Zhang, D., Wang, K.: Fisherpalms Based Palmprint Recognition. Pattern Recognition Letters 24(15), 2829–2838 (2003)
Duta, N., Jain, A.K., Mardia, K.V.: Matching of Palmprint. Pattern Recognition Letters 23(4), 477–485 (2002)
You, J., Li, W., Zhang, D.: Hierarchical Palmprint Identification via Multiple Feature Extraction. Pattern Recognition 35(4), 847–859 (2002)
Wang, Y., Ruan, Q.: Kernel Fisher Discriminant Analysis for Palmprint Recognition. In: IEEE The 18th Int. Conf. on Pattern Recognition, ICPR’06, pp. 457–460 (2006)
Jiang, W., Tao, J., Wang, L.: A Novel Palmprint Recognition Algorithm Based on PCA and FLD. In: IEEE, Int. Conf. on Digital Telecommunications, August 2006, pp. 28–32 (2006)
Lu, G., Zhang, D., Wang, K.: Palmprint Recognition Using Eigenpalms Features. Pattern Recognition Letters 24(9-10), 1463–1467 (2003)
Zhang, D., Kongi, W., You, J., Wong, M.: Online Palmprint Identification. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(9), 1041–1049 (2003)
Scholkopf, B., Somala, A.: Learning with Kernel: Support Vector Machine, Regularization, Optimization and Beyond. MIT Press, Cambridge (2002)
Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)
Chang, T., Kuo, C.J.: Texture Analysis and Classification with Tree-Structured Wavelet Transform. IEEE Trans. Image Processing 2(4), 429–441 (1993)
Zhang, B., Zhang, H., Sam, S.: Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory. IEEE Transactions on Neural Networks 15(1), 166–177 (2004)
Chien, J., Wu, C.: Discriminant Waveletfaces and Nearest Feature Classifier for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1644–1649 (2002)
Li, W., Gong, W., Yang, L., Chen, W., Gu, X.: Facial Feature Selection Based on SVMs by Regularized Risk Minimization. In: IEEE, The 18th Conference on Pattern Recognition (ICPR’06), pp. 540–543 (2006)
Kumar, A., Zhang, D.: Personal Recognition Using Hand Shape and Texture. IEEE Transactions on Image Processing 5(8), 2454–2460 (2006)
Jing, X.Y., Zhang, D.: A Face and Palmprint Recognition Approach Based on Discriminant DCT Feature Extraction. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 34(6), 2405–2415 (2004)
Zhang, L., Zhang, D.: Characterization of Palmprints by Wavelet Signatures via Directional Context Modeling. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics 34(3), 1335–1347 (2004)
Valentin, T.: Face-space Models of Face Recognition. In: Computational, Geometric, and Process Perspectives on Facial Cognition: Context and Challenges, Lawrence Erbaum, Hillsdale (1999)
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Ekinci, M., Aykut, M. (2007). A Novel Approach for Automatic Palmprint Recognition. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_11
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DOI: https://doi.org/10.1007/978-3-540-72665-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72664-7
Online ISBN: 978-3-540-72665-4
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