Towards Expression-invariant Face Recognition Using Multiple Adaptive Attributes

The performances of most existing face recognition systems suffer from facial expressions. Unfortunately, there is not yet a satisfactory solution. Therefore, the main focus of this thesis is on expression-invariant face recognition algorithms. In this thesis, we first propose a 2D face recognition algorithm by separately modeling geometry and texture information in a face image. The effect of expression is removed from each of these two attributes independently. We then re-combine them to construct a robust face identifier. Then, we extend our algorithm to recognize 3D faces using multiple geometric attributes in a face mesh, taking advantage of the invariance of 3D geometry under poses and illuminations. In order to adapt to expression variations, training is performed for each geometric attribute as well as the weighting scheme for combining multiple attributes. Using our proposed algorithm, the recognition ratio exceeds 96% for the challenging GavaDB database.