Multi-Modal Face Recognition

In this paper, we exploit the multi-modal face recognition capability by a comparative study on 6 fusion methods in the score level, which can be divided into 2 kinds: (1) simple fusion without data training, such as Sum, Product, Max and Min; (2) complex fusion including a predefined data training section, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Our experiments are based on the CASIA 3D Face Database and can be divided into two modes: verification and classification. Major conclusions are: (1) 2D modality can achieve similar performance as to 3D modality, and fusion scheme can substantially improve the recognition performance; (2) Product rule gives the best recognition performance in simple fusion methods without training stage; (3) There is no guarantee that the complicated fusion methods will achieve better recognition performance than the simple fusion methods, and it is important to select the most suitable model for fusion according to the tasks.