3D Face Decomposition and Region Selection Against Expression Variations

3D face recognition exploits shape information as well as texture information in 2D systems. The use of whole 3D face is sensitive to some undesired situations like expression variations. To overcome this problem, we investigate a new approach that decomposes the whole 3D face into sub-regions and independently extracts features from each sub-region. 3D DCT is applied to each sub-region and most discriminating DCT coefficients are selected. The nose region gives the most contribution to the list of discriminating coefficients. Furthermore, a better recognition rate is achieved by only using the nose region. The highest recognition score in our experiments is 98.97% where rank-one recognition rates are considered. The results of the proposed approach are compared to other methods that use FRGC v2 database.