Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization.

Shahenda Sarhan,Aida A. Nasr,Mahmoud Y. Shams

doi:10.1155/2020/8821868

Abstract

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

Highlights

Face recognition has become one of the most discussed topics in the computer vision field, it is not new but with the revolution in electronic devices as mobile phones researchers have realized that it can be a solution to different issues
We introduce a combined adaptive deep learning vector quantization classifier (CADLVQ) with different parameters sets, in which we seek for changing the entire network parameters of adaptive deep learning vector quantization (ADLVQ) to increase the recognition accuracy
Acceptance Rate (GAR) of the tested multipose face images based on CADLVQ classifier is shown in Table 3 from which we can see that five parameter sets were used; V1 represents the frontal view of the face image and V2 and V3 represents different views of the face images. e influence of multipose face images (V1, V2, and V3) on the recognition process with SDUMLA-HMT and CASIA-V5 datasets for CADLVQ

Summary

Introduction

Face recognition has become one of the most discussed topics in the computer vision field, it is not new but with the revolution in electronic devices as mobile phones researchers have realized that it can be a solution to different issues. We introduce a combined adaptive deep learning vector quantization classifier (CADLVQ) with different parameters sets, in which we seek for changing the entire network parameters of adaptive deep learning vector quantization (ADLVQ) to increase the recognition accuracy. (1) Proposing a combined adaptive deep learning vector quantization classifier to boost the weakness of the ADLVQ classifier (2) Enhancing the stability and reliability of the CADLVQ based multipose face images using different parameters sets (3) Comparing the proposed CADLVQ with the most recent deep learning approaches using matching scores, weighted sum, weighted product, and majority voting (4) Handling colluded face images with different block sizes by predicting the missing features using expectation maximization (EM) algorithm is paper is organized as follows.

Related Work

Combined Adaptive Deep Learning Vector Quantization

Results and Discussion

Conclusion and Future Work