Face recognition by compensation of illumination and pose variations

We propose a novel 2D image-based approach that can simultaneously handle illumination and pose variations to enhance face recognition rate. It is much simpler, requires much less computational effort than the methods based on 3D models, and provides a comparable or better recognition rate.

In this paper, we propose a new approach based on 2D images for handling illumination and pose variations simultaneously. We first propose a simple pose estimation method based on 2D images, which uses a suitable classification rule and image representation to classify a pose of a face image. In order to represent the characteristic of each pose class, we transform a face image into an edge image, in which facial components such as eyes, nose and mouth in the image are enhanced. Then, the image can be assigned to a pose class by a classification rule in a low-dimensional subspace constructed by a feature extraction method. On the other hands, unlike general classification problems, pose classes can be placed sequentially from left profile to right profile in the pose space, and we can make use of the order relationship between classes. Therefore, in order to model the continuous variation in head pose, we investigate the performance of feature extraction methods for regression problems and classification problems where classes have an order relationship. Second, we propose a shadow compensation method that compensates for illumination variation in a face image so that the image can be recognized by a face recognition system designed for images under normal illumination condition. Generally, human faces are similar in shape in that they are comprised of two eyes, a nose and a mouth. Each of these components forms a shadow on a face, showing distinctive characteristics depending on the direction of light in a fixed pose. By using such characteristics generated by the shadow, we can compensate for illumination variation on a face image caused by the shadow and obtain a compensated image that is similar to the image taken under frontal illumination. Since the direction of light can change continuously, it is insufficient to represent the illumination variation with the shadow characteristic from only one discredited light category. Thus, we use more than one shadow characteristics to compensate for illumination variation by giving an appropriate weight to each estimated light category. Furthermore, we extend the compensation method that works not only for the frontal pose class but also for other pose classes as well. These shadow compensated images in each pose class are used for face recognition.