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

In the domain of image processing, face recognition is one of the most well-known research field. When humans have very similar biometric properties, such as identical twins, the face recognition system is considered as a challengeable problem. In this paper, the AdaBoost method is utilized to detect the facial area of input image. After that the facial area is divided into some local regions. Finally, new efficient facial-based identical twins feature extractor based on the geometric moment is applied into local regions of face image. The used feature extractor is Pseudo-Zernike Moment (PZM) which is employed inside the local regions of facial area of identical twins images. To evaluate the proposed method, two datasets, Twins Days Festival and Iranian Twin Society, are collected where the datasets includes scaled and rotated facial images of identical twins in different illuminations. The experimental results demonstrates the ability of proposed method to recognize a pair of identical twins in different situations such as rotation, scaling and changing illuminatio