22 September 2016 Sparse coding joint decision rule for ear print recognition
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Human ear recognition has been promoted as a profitable biometric over the past few years. With respect to other modalities, such as the face and iris, that have undergone a significant investigation in the literature, ear pattern is relatively still uncommon. We put forth a sparse coding-induced decision-making for ear recognition. It jointly involves the reconstruction residuals and the respective reconstruction coefficients pertaining to the input features (co-occurrence of adjacent local binary patterns) for a further fusion. We particularly show that combining both components (i.e., the residuals as well as the coefficients) yields better outcomes than the case when either of them is deemed singly. The proposed method has been evaluated on two benchmark datasets, namely IITD1 (125 subject) and IITD2 (221 subjects). The recognition rates of the suggested scheme amount for 99.5% and 98.95% for both datasets, respectively, which suggest that our method decently stands out against reference state-of-the-art methodologies. Furthermore, experiments conclude that the presented scheme manifests a promising robustness under large-scale occlusion scenarios.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Guermoui Mawloud, Guermoui Mawloud, Djamel Melaab, Djamel Melaab, Mohamed Lamine Mekhalfi, Mohamed Lamine Mekhalfi, } "Sparse coding joint decision rule for ear print recognition," Optical Engineering 55(9), 093105 (22 September 2016). https://doi.org/10.1117/1.OE.55.9.093105 . Submission:


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