D Face Recognition Method Using 2 DPCA-Euclidean Distance Classification

In this paper, we present a 3d face recognition method that is robust to changes in facial expressions. Instead of locating many feature points, we just need to locate the nose tip as a reference point. After finding this reference point, pictures are converted to a standard size. Two dimensional principle component analysis (2DPCA) is employed to obtain features Matrix vectors. Finally Euclidean distance method is employed for classifying and comparison of the features. Experimental results implemented on CASIA 3D face database which including 123 individuals in total, demonstrate that our proposed method achieves up to 98% recognition accuracy with respect to pose variation.