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Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Hima Bindu (Department of Electronics and Communication Engineering, GITAM University, Hyderabad, India)
Manjunathachari K. (Department of Electronics and Communication Engineering, GITAM University, Hyderabad, India)

Sensor Review

ISSN: 0260-2288

Article publication date: 25 January 2018

Issue publication date: 24 May 2018

Abstract

Purpose

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

Design/methodology/approach

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

Findings

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

Originality/value

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Keywords

Citation

Bindu, H. and K., M. (2018), "Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition", Sensor Review, Vol. 38 No. 3, pp. 269-281. https://doi.org/10.1108/SR-06-2017-0115

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited