Abstract
Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and l1-norm to present a powerful error estimator that gains flexibility and robustness to various contaminations by cooperatively detecting and correcting errors. Furthermore, we equip the error estimator with a tailored discriminative nonnegative sparse regularizer to extract significant nonnegative features. We manage to explore an analytical optimization approach regarding this unified scheme and figure out a novel efficient method to address the challenging non-negative constraint. Finally, the proposed coding method is extended for robust multispectral palmprint recognition. Namely, we develop a constrained particle swarm optimizer to search for the feasible parameters to fuse the extracted robust features of different spectrums. Extensive experimental results on both contactless and contact-based multispectral palmprint databases verify the flexibility and robustness of our methods.
Highlights
Biometrics, like face, fingerprint, and iris images, have been exhaustively investigated for identity verification [1]
By extracting the centroids and variation of the training samples, Deng et al proposed a superposed sparse representation classifier (SRC) (SSRC) [26]. These ideas improved the representation ability of the dictionaries, they can not overcome the drawback of the regularization-based methods, which leads to their limited robustness
We introduce CDNSC from the following aspects: cooperative error estimator, discriminative nonnegative sparse regularizer, the optimization of CDNSC, and the extended CDNSC
Summary
Biometrics, like face, fingerprint, and iris images, have been exhaustively investigated for identity verification [1]. Palmprint recognition methods can be roughly divided into categories [3] such as texture modeling-based [4,5,6,7,8,9], subspace learning-based [10,11,12,13], and local descriptor-based [14,15,16,17,18]. These three categories of methods attempt to extract critical features by ideally defined transformations, principal directions, or descriptors. What’s more, despite a little work that merely considers palmprint image degeneration due to the objective rotation and illumination variation [15,19], most of the methods neglect to consider robust palmprint recognition because of the potential occlusion and corruption in real-world applications
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