There has been significant progress in the field of automatic ear recognition, wherein ear images... more There has been significant progress in the field of automatic ear recognition, wherein ear images are captured in a constrained environment. But unconstrained ear recognition have acquired less attention due to the unavailability of such a database having variations in illumination, pose, size, resolution and occlusions. It is a challenging pattern recognition problem due to large intra-class variability. In this paper, we propose a novel local descriptor for unconstrained ear recognition based on scattering wavelet network (ScatNet) to extract translation and small deformation invariant local features. The experiments conducted on a recently released unconstrained ear benchmark databases, such as Annotated Web Ears (AWE) and USTB-Helloear databases, and also on our newly created IIT-Bombay smartphone-captured ear database show the effectiveness and robustness of the proposed local feature descriptor in terms of Equal Error Rate (EER) and Rank-1 (R1) accuracy.
There has been significant progress in the field of automatic ear recognition, wherein ear images... more There has been significant progress in the field of automatic ear recognition, wherein ear images are captured in a constrained environment. But unconstrained ear recognition have acquired less attention due to the unavailability of such a database having variations in illumination, pose, size, resolution and occlusions. It is a challenging pattern recognition problem due to large intra-class variability. In this paper, we propose a novel local descriptor for unconstrained ear recognition based on scattering wavelet network (ScatNet) to extract translation and small deformation invariant local features. The experiments conducted on a recently released unconstrained ear benchmark databases, such as Annotated Web Ears (AWE) and USTB-Helloear databases, and also on our newly created IIT-Bombay smartphone-captured ear database show the effectiveness and robustness of the proposed local feature descriptor in terms of Equal Error Rate (EER) and Rank-1 (R1) accuracy.
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