Face Recognition Based on Illumination Adaptive LDA

The variation of facial appearance due to the illumination degrades face recognition systems considerably, which is well known as one of the bottlenecks in face recognition. However, the variations of each subject which are due to the changes of illumination are extremely similar to each other. We offline collect many face classes each of which has many images under different lighting conditions, a common within-class scatter matrix describing the within-class illumination variations of all the face classes can be gotten. Based on this, illumination adaptive linear discriminant analysis (IALDA) is proposed to solve illumination variation problems in face recognition when each face class has only one training sample under the standard lighting conditions. In the IALDA method, the illumination direction of an input face image is firstly estimated. Then the corresponding LDA feature, which is robust to the variations between the images under the estimated lighting conditions and the standard lighting conditions, is extracted. Experiments on the face databases demonstrate the effectiveness of the proposed method.