Using nonlocal filtering and feature extraction approaches in three-dimensional face recognition by Kinect

Abstract

To use low-cost depth sensors such as Kinect for three-dimensional face recognition with an acceptable rate of recognition, the challenges of filling up nonmeasured pixels and smoothing of noisy data need to be addressed. The main goal of this article is presenting solutions for aforementioned challenges as well as offering feature extraction methods to reach the highest level of accuracy in the presence of different facial expressions and occlusions. To use this method, a domestic database was created. First, the noisy pixels-called holes-of depth image is removed by solving multiple linear equations resulted from the values of the surrounding pixels of the holes. Then, bilateral and block matching 3-D filtering approaches, as representatives of local and nonlocal filtering approaches, are used for depth image smoothing. Curvelet transform as a well-known nonlocal feature extraction technique applied on both RGB and depth images. Two unsupervised dimension reduction techniques, namely, principal component analysis and independent component analysis, are used to reduce the dimension of extracted features. Finally, support vector machine is used for classification. Experimental results show a recognition rate of 90% for just depth images and 100% when combining RGB and depth data of a Kinect sensor which is much higher than other recently proposed algorithms.