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

摘要

In the world of ubiquitous computing, fuzzy logic has been emerged as an important research area in the field of face recognition (FR) applications. In this paper, a new efficient and advanced method; inspired from interval type-II fuzzy membership concept of fuzzy logic is proposed. The motivation behind our method is to exploit the benefit of an extended interval type-II membership function: a new concept to fuzzy logics; in collaboration with kernel based sparse representation for FR To integrate all the pluton, we propose a method called: extended interval type-II and kernel based sparse representation method (ExIntTy2KBSRM). In our proposed method, first we figure out the measure of participation of individual pixels in identifying face images using a variant of Interval type-II fuzzy logic i.e. extended interval type-II membership function in place of type 1 fuzzy logic. Next, we calculate K nearest neighbor to training specimens using simple Euclidean distance metric for sparse representation of each test specimen as a combinatorial of calculated K nearest training specimens. After this, we do classification based on contribution made by calculated train specimens in representation results. The efficacy and effectiveness of our proposed method is shown based on experiments performed on various standard databases. The experimental results show that our method deal with challenges of face recognition more efficiently as compared to other state of art methods, as it integrates the pluton of two different membership function based extended interval type-II fuzzy logics in collaboration with kernel sparse representation. Also, our experimental analysis tells that our proposed method improves the classification accuracy to 2-10% greater than the other existing relevant methods.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. (C) 2018 Elsevier Ltd. All rights reserved.