Subspace Approximation of Face Recognition Algorithms: An Empirical Study

We present a theory for constructing linear subspace approximations to face-recognition algorithms and empirically demonstrate that a surprisingly diverse set of face-recognition approachescanbeapproximatedwellbyusingalinearmodel.Alinear model, built using a training set of face images, is specified in terms of a linear subspace spanned by, possibly nonorthogonal vectors. We divide the linear transformation used to project face images into this linear subspace into two parts: 1) a rigid transformation obtained through principal component analysis, followed by a nonrigid,affinetransformation.The constructionof theaffinesubspace involves embedding of a training set of face images constrained by the distances between them, as computed by the face-recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical MDS. Any new face image is projected into this embedded space using an affine transformation. We empirically demonstrate th...

• Type: Journal
• Year: 2008
• Where: TIFS
• Authors: Pranab Mohanty, Sudeep Sarkar, Rangachar Kasturi, P. Jonathon Phillips
• Keywords: Face Image, Face-recognition, Linear Subspace, TIFS 2008