A novel facial image recognition method based on perceptual hash using quintet triple binary pattern

Turker Tuncer,Moloud Abdar,Sengul Dogan,Paweł Pławiak

doi:10.1007/s11042-020-09439-8

Abstract

Image classification (categorization) can be considered as one of the most breathtaking domains of contemporary research. Indeed, people cannot hide their faces and related lineaments since it is highly needed for daily communications. Therefore, face recognition is extensively used in biometric applications for security and personnel attendance control. In this study, a novel face recognition method based on perceptual hash is presented. The proposed perceptual hash is utilized for preprocessing and feature extraction phases. Discrete Wavelet Transform (DWT) and a novel graph based binary pattern, called quintet triple binary pattern (QTBP), are used. Meanwhile, the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms are employed for classification task. The proposed face recognition method is tested on five well-known face datasets: AT&T, Face94, CIE, AR and LFW. Our proposed method achieved 100.0% classification accuracy for the AT&T, Face94 and CIE datasets, 99.4% for AR dataset and 97.1% classification accuracy for the LFW dataset. The time cost of the proposed method is O(nlogn). The obtained results and comparisons distinctly indicate that our proposed has a very good classification capability with short execution time.

Highlights

Nowadays, diverse biometric methods have been utilized for authentication in security priority systems [59, 61] in which facial images classification/recognition is one of the mostly used of them [44] image classification/categorization can be considered as one of influential tasks in the domain of machine learning [52,53,54, 68] and computer vision that has been broadly attracted vivid attention from researchers worldwide [3]
The face recognition methods have been used in a wide range of subjects for instance military, social media, mobile platforms, urbanism [18, 34, 44, 50] To do so, different machine learning methods have been utilized for face recognition and the most important face recognition methods are listed as follows:
To extract discriminative and informative features, we presented a new local graph structures (LGS) in this paper

Summary

Introduction

Diverse biometric methods have been utilized for authentication in security priority systems [59, 61] in which facial images classification/recognition is one of the mostly used of them [44] image classification/categorization can be considered as one of influential tasks in the domain of machine learning [52,53,54, 68] and computer vision that has been broadly attracted vivid attention from researchers worldwide [3]. Image processing methods have been widely applied to the facial image for authentication in the literature [43]. Local pattern-based face recognition methods perform recognition using facial salient features. Local pattern-based facial recognition methods create a special pattern and scan the texture image with this pattern. Facial image recognition is one of the mostly used biometrics methods in the literature and proposing a hand-crafted and effective recognition method is one of the crucial problem for face recognition

Methods

Results

Conclusion