3D face recognition using multiple features for local depth information

Depth information is one of the most important factors for the recognition of a digital face image. Range images are very useful, when comparing one face with other faces, because of implicating depth information. As the processing for the whole face produces a lot of calculations and data, face images can be represented in terms of a vector of feature descriptors for a local area. In this paper, depth areas of a 3 dimensional (3D) face image were extracted by the contour line from some depth value. These were resampled and stored in consecutive location in feature vector using multiple feature method. A comparison between two faces was made based on their distance in the feature space, using Euclidian distance. This paper reduced the amount of index data in the database and used fewer feature vectors than other methods. The proposed algorithm can be highly recognized for using local depth information and less feature vectors on the face.