Using backpropagation neural network for face recognition with 2D+3D hybrid information
Biometric measurements received an increasing interest for security applications in the last two decades. After the 911 terrorist attacks, face recognition has been an active research in this area. However, very few research group focus on face recognition from both 2D and 3D facial images. Almost all existing recognition systems rely on a single type of face information: 2D intensity (or color) image or 3D range data set [Wang, Y., Chua, C., & Ho, Y. (2002). Facial feature detection and face recognition from 3D and 3D images. Pattern Recognition Letters, 23, 11911202]. The objective of this study is to develop an effective face recognition system that extracts and combines 2D and 3D face features to improve the recognition performance. The proposed method derived the information of 3D face (disparity face) using a designed synchronous Hopfield neural network. Then, we retrieved 2D and 3D face features with principle component analysis (PCA) and local autocorrelation coefficient (LAC) respectively. Eventually, the information of features was learned and classified using backpropagation neural networks. An experiment was conducted with 100 subjects, and for each subject thirteen stereo face images were taken with different expressions. Among them, seven faces with expressions were used for training, and the rest of the expressions were used for testing. The experimental results show that the proposed method effectively improved the recognition rate by combining the 2D with 3D face information.
backpropagation neural network face recognition disparity face principle component analysis (pca) local autocorrelation coefficients (lac) stereovision