抽象的

Surveillance Mining System for Low Resolution Face Image Recognition Using Kernel Coupling

Dr. B. F. Momin, Mr. R. J. Datir

Video surveillance systems for face recognition are confronted with low-resolution face images. Low resolution face images coming from real time video does not give discriminant information to identify similar images in a dataset. Traditional method solved this problem through employing super- resolution (SR). But these are time-consuming, sophisticated SR algorithms. These algorithm are not suitable for real-time applications. To avoid the limitations, in this work, new feature extraction method for LR faces called coupled kernel distance metric learning (KCDML) is proposed without any SR pre-processing. By using a kernel trick and a specialized locality preserving criterion, we formulated the problem of coupled kernel embedding as an optimization problem whose aims are to search for the pair-wise sample staying as close as possible and to preserve the local structure intrinsic data geometry. Instead of an iterative solution, one single generalized Eigen- decomposition can be leveraged to compute the two transformation matrices for two classifications of data sets.