Performance evaluation on image fusion techniques for face recognition
by Aniruddha Dey; Shiladitya Chowdhury; Jamuna Kanta Sing
International Journal of Computational Vision and Robotics (IJCVR), Vol. 8, No. 5, 2018

Abstract: In face recognition, a feature vector usually represents the salient characteristics that best describe a face image. However, these characteristics vary quite substantially while looking into a face image from different directions. Therefore, by accumulating these directional features into a single feature vector will certainly lead to superior performance. This paper addresses this issue by means of image fusion and presents a comprehensive performance analysis of different image fusion techniques for face recognition. Image fusion is done between the original captured image and its true/partial diagonal images. The fusion is made by three different ways by placing the images: 1) one-over-other (superimposed); 2) side-by-side (horizontally); 3) up-and-down (vertically). The empirical results on publicly available AT&T, UMIST and FERET face databases collectively demonstrate that superimposed image between the original and its true diagonal images actually provides superior discriminant features for face recognition as compared to either original or its diagonal image.

Online publication date: Fri, 28-Sep-2018

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