Extended interval type-II and kernel based sparse representation method for face recognition

Impact and significance of the proposed method: The main impact of the proposed method in FR based expert and intelligent systems is that, it considers unseen information available in pixel values of a face image present due to non-linear variations and overlapping of pixels. It also contains the advantage of spatial similar structure information present in face images. This makes the system more effective and efficient in processing face images for FR. Our method also works where Gaussian membership function does not work and discretizes linear and non-linear functions appropriately. Thus, the proposed method is universally applicable to solve more challenges of FR viz. illumination, occlusion, expression etc. and also has application in other areas viz. medical image processing, decision making problems, hand written words recognition, speech processing, watermarking etc. Also, our method makes FR systems computationally more efficient and cost effective by using sparse concept to matrices, which makes system to consume less memory and process data faster.