Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local information to develop a robust face recognition system.
In this papers it is proposed that hybridization of global and local facial features and combination of 2D and 3D modality helps in improving performance of face recognition system. The main issue of existing face recognition systems is the high false accept rate which is not desirable when security is the main concern. Most of the existing face recognition techniques overcome these problems with some constraints. However, the proposed methodology has achieved better results and handled all the three issues successfully. Also the use of 2.5D images (Depth Map) and dimensionality reduced data (Eigen faces) has shown that the system is computationally reasonable.