Features-Level Fusion of Reflectance and Illumination Images in Finger-Knuckle-Print Identification System

Abstract

In Finger-Knuckle-Print (FKP) recognition, feature extraction plays a very important role in the overall system performance. This paper merges two types of the histograms of oriented gradients (HOG)-based features extracted from reflectance and illumination images for FKP-based identification. The Adaptive Single Scale Retinex (ASSR) algorithm has been used to extract the illumination and the reflectance images from each FKP image. Serial feature fusion is used to form a large feature vector for each user, and extract the distinctive features in the higher-dimension vector space. Finally, the cosine similarity distance measure is used for classification. The Hong Kong Polytechnic University (PolyU) FKP database is used during all of the tests. Experimental results show that our proposed system achieves better results than other state-of-the-art system.