Pose and illumination invariant 2D to 3D facial recognition system
Ukil Yang, Hyoungchul Shin, Kwanghoon Sohn · 2008
This paper proposes a pose and illumination invariant face recognition method based on a 2D to 3D facial recognition system which uses two dimensional (2D) image as an input and three dimensional (3D) data as a database. To improve the performance of a facial recognition system, we reorganize the framework of a conventional recognition system into more suitable framework for a 2D to 3D facial recognition system. 2D to 3D pose and illumination estimation algorithm is proposed based on a learning algorithm using multilayer perceptron (MLP). The proposed method estimates both pose and illumination factors of an input image in real-time, and compensates for a database in order to overcome the problems of pose and illumination without any occlusion occurred by insufficient information. To evaluate the performance, we performed both the accuracy tests of pose and illumination estimation and the recognition tests of 2D to 3D facial recognition system with a face database containing both two and three dimensional data.