A low-dimensional representation of human faces for arbitrary lighting conditions

When recognizing a fixed object from a fixed viewpoint, the dominant source of variation in image intensity is lighting changes. We propose a low-dimensional model for human faces that can both synthesize a face image when given lighting conditions and can estimate lighting conditions when given a face image. The model can handle non-Lambertian and self-shadowing surfaces such as faces because it does not make any assumptions about either the surface geometry or bidirectional reflectance function. The model can be adapted to handle any arbitrary lighting condition, and is easily extendable to any other viewpoint or to any other object