Skip to Main Content
Using a novel data dimension reduction method proposed in statistics, we develop an appearance-based face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as coordinate in a high-dimensional space. However, since faces are not truly Lambertian surfaces and indeed produce self-shadowing, images deviates from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation using Sliced inverse regression (SIR) [K.C. Li, 1991]. Our face recognition algorithm termed as Sirface produces well-separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expression. Sirface is shown to be equivalent to the well known Fisherface algorithm [P.N. Belhumeur, et al., 1997] in the subspace sense. However, Sirface is shown to produce the optimal reduced subspace (with the fewest dimensions) resulting in a lower error rate and reduced computational expense. Experimental results comparing Sirface to Fisherface on the Yale face database are presented.