Discriminative Local Feature Fusion for Ear Recognition Problem
Ibrahim Omara, Xiaoming Li, Gang Xiao, Khan Adil, Wangmeng Zuo · 2018
Ear recognition problem is known as selecting whether two ear images belong to the same person or not, this consider as a challenge due to variation in lighting, background, pose, scale, and occlusion. This paper presents an improvement method for unconstrained ear recognition problem based on local feature fusion, and further analyzes the performance and efficiency of discriminative local feature fusion for aligned and non-aligned ear images. Firstly, local discriminative features such as LPQ, HOG, LBP, POEM, BSIF and Gabor features are extracted from the ear images. Then, Discriminant Correlation Analysis (DCA) is exploited for fusion and reduction dimension. Finally, support vector machine (SVM) is adopted for classification. Experiments are conducted on popular ear databases, USTB I, USTB II, and IIT Delhi II. Furthermore, we report an encouraging result on a difficult and challenging ear database called annotated web ear (AWE) that is collected from the wild. The experimental results show superior of proposed approach that can achieve a high performance for non-aligned images (AWE and USTB II datasets), on the other hand, unique local features can achieve promising recognition rates for aligned images, USTB I and IIT Delhi II datasets.