An MKD-SRC Approach for Face Recognition from Partial Image

Face recognition has received a great deal of attention from the scientific and industrial communities over the past several decades owing to its wide range of applications in information security and access control, law enforce, surveillance and more generally image understanding. A general partial face recognition method based on Multi-Key point Descriptors (MKD) that does not require face alignment by eye coordinates or any other fiducial points. The invariant shape adaptation makes image matching more robust to viewpoint changes which are desired in face recognition with pose variations. A Multi-Key point Descriptors (MKD), where the descriptor size of a face is determined by the actual content of the image. The MKD-SRC (Sparse Representation-based Classification) framework that works for both holistic faces and partial faces can be sparsely represented by a large dictionary of gallery descriptors. Multitask sparse representation is learned for an each probe face and the Sparse Representation-based Classification (SRC) approach is applied for face recognition a fast atom filtering strategy for MKD-SRC to address large-scale face recognition (with 10,000 gallery images). A new key point descriptor called Gabor Ternary Pattern (GTP) / Directional Local Extreme Pattern (DLEP) is developed for robust an discriminative face recognition.