Ear recognition using local color texture descriptors from one sample image per person

Morphological shape of the human ear presents a rich and stable information embedded on the curved 3D surface, which has invited lot attention from the forensic and engineer scientists in order to differentiate and recognize people. However, recognizing identity from morphological shape of the human ear using one sample image per person in training-set, with insufficient and incomplete training data, dealing with strong person-specificity can be very challenging. To address such problem, we propose a simple yet effective approach which uses and exploits local color texture descriptors in order to achieve faster and more accurate results. Support Vector Machine (SVM) is used as a classifier. We experiment with USTB-1 database consisting of several RGB ear benchmarks of different natures taken under varying conditions and imaging qualities. The experiments show excellent results beyond the state-of-the-art.