FaceFilter: Face Identification with Deep Learning and Filter Algorithm

Mohammed Alghaili,Zhiyong Li,Hamdi A R Ali

doi:10.1155/2020/7846264

Mohammed Alghaili, Zhiyong Li + Show 1 more

Open Access

https://doi.org/10.1155/2020/7846264

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Journal: Scientific programming	Publication Date: Aug 1, 2020
Citations: 11	License type: CC BY 4.0

Affiliation: Hunan University, University of Sharjah

Abstract

Although significant advances have been made recently in the field of face recognition, these have some limitations, especially when faces are in different poses or have different levels of illumination, or when the face is blurred. In this study, we present a system that can directly identify an individual under all conditions by extracting the most important features and using them to identify a person. Our method uses a deep convolutional network that is trained to extract the most important features. A filter is then used to select the most significant of these features by finding features greater than zero, storing their indices, and comparing the features of other identities with the same indices as the original image. Finally, the selected features of each identity in the dataset are subtracted from features of the original image to find the minimum number that refers to that identity. This method gives good results, as we only extract the most important features using the filter to recognize the face in different poses. We achieve state-of-the-art face recognition performance using only half of the 128 bytes per face. The system has an accuracy of 99.7% on the Labeled Faces in the Wild dataset and 94.02% on YouTube Faces DB.

Highlights

Deep neural networks and especially convolutional neural networks (CNNs) have become the most commonly used method for feature representation and have achieved good results in face recognition problems
Face recognition can be divided into two categories: face verification, where two faces are presented and the system needs to verify whether these two faces belong to the same person, and face identification, where a face image is presented with an unknown identity and the system needs to determine this identity
Previous approaches to face recognition that are based on the discriminative classification model are trained on a dataset of known identities, and an intermediate bottleneck layer is used as a representation for recognition. is approach generalizes a very large representation for each face, but some works have tried to reduce this dimensionality using PCA [10]

Summary

Introduction

Deep neural networks and especially convolutional neural networks (CNNs) have become the most commonly used method for feature representation and have achieved good results in face recognition problems. Most existing works that have focused on face recognition have achieved a high level of success [1,2,3,4,5,6,7,8,9,10,11,12,13]. Is approach generalizes a very large representation for each face, but some works have tried to reduce this dimensionality using PCA [10]. Another approach used in FaceNet [14] directly trained its output to obtain 128-D embedding using a triplet-based loss function based on LMNN [9]. Previous approaches to face recognition that are based on the discriminative classification model (face identification) are trained on a dataset of known identities, and an intermediate bottleneck layer is used as a representation for recognition. is approach generalizes a very large representation for each face, but some works have tried to reduce this dimensionality using PCA [10].

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