Zernike Moment-Based Feature Extraction for Facial Recognition of Identical Twins

Abstract

Face recognition is one of the most challenging problems in the domain of image processing and machine vision. The face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed according to geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled and rotated facial images of identical twins in different illuminations. The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show that the proposed method is robust to rotation, scaling and changing illumination.