Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Three-Dimensional Model Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Three-Dimensional Face Recognition
International Journal of Computer Vision
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
3D face recognition based on facial shape indexes with dynamic programming
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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A face has its structural components such as eyes, nose and mouth. Availability of depth and facial shape information of a face is one of the main advantages of three-dimensional (3D) face recognition. In order to utilize the depth information, we extract rigid facial points on facial components and their relational features. We also extract shape indexes on areas around rigid points to represent curvature information of a face. We perform face recognition by using weighted distance matching, Support Vector Machine (SVM) and Independent Component Analysis (ICA) with three different sets of features. From the experimental results, the proposed feature set performs the best compared with the other feature sets for all tested classifiers. The experimental results also show that using of both the position and the curvature features can represent a face effectively and distinctively while each of them does not provide a good discrimination power for face recognition individually.