A Hybrid Method of Enhancing Accuracy of Facial Recognition System Using Gabor Filter and Stacked Sparse Autoencoders Deep Neural Network

Face recognition has grown in popularity due to the ease with which most recognition systems can find and recognize human faces in images and videos. However, the accuracy of the face recognition system is critical in ascertaining the success of a person’s identification. A lack of sufficiently larg...

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Published in:	Applied sciences Vol. 12; no. 21; p. 11052
Main Authors:	Jaber, Abdullah Ghanim, Muniyandi, Ravie Chandren, Usman, Opeyemi Lateef, Singh, Harprith Kaur Rajinder
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2022
Subjects:	Accuracy Algorithms Bandwidths Datasets Deep learning deep neural network Efficiency face recognition Facial recognition technology Gabor filter Human subjects hybrid method Localization Methods Neural networks stacked sparse autoencoders
ISSN:	2076-3417, 2076-3417
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Abstract	Face recognition has grown in popularity due to the ease with which most recognition systems can find and recognize human faces in images and videos. However, the accuracy of the face recognition system is critical in ascertaining the success of a person’s identification. A lack of sufficiently large training datasets is one of the significant challenges that limit the accuracy of face recognition systems. Meanwhile, machine learning (ML) algorithms, particularly those used for image-based face recognition, require large training data samples to achieve a high degree of face recognition accuracy. Based on the above challenge, this research proposes a method for improving face recognition precision and accuracy by employing a hybrid approach of the Gabor filter and a stacked sparse autoencoders (SSAE) deep neural network. The face image datasets from Olivetti Research Laboratory (OLR) and the Extended Yale-B databases were used to evaluate the proposed hybrid model’s performance. All face image datasets used in our experiments are grayscale image type with a resolution of 92 × 112 for the OLR database and a resolution 192 × 168 for the Extended Yale-B database. Our experimental results showed that the proposed method improved face recognition accuracy by approximately 100% for the two databases used at a significantly reduced feature extraction time compared to the current state-of-art face recognition methods for all test cases. The SSAE approach can explore large and complex datasets with minimal computation time. In addition, the algorithm minimizes the false acceptance rate and improves recognition accuracy. This implies that the proposed method is promising and has the potential to enhance the performance of face recognition systems.
AbstractList	Face recognition has grown in popularity due to the ease with which most recognition systems can find and recognize human faces in images and videos. However, the accuracy of the face recognition system is critical in ascertaining the success of a person’s identification. A lack of sufficiently large training datasets is one of the significant challenges that limit the accuracy of face recognition systems. Meanwhile, machine learning (ML) algorithms, particularly those used for image-based face recognition, require large training data samples to achieve a high degree of face recognition accuracy. Based on the above challenge, this research proposes a method for improving face recognition precision and accuracy by employing a hybrid approach of the Gabor filter and a stacked sparse autoencoders (SSAE) deep neural network. The face image datasets from Olivetti Research Laboratory (OLR) and the Extended Yale-B databases were used to evaluate the proposed hybrid model’s performance. All face image datasets used in our experiments are grayscale image type with a resolution of 92 × 112 for the OLR database and a resolution 192 × 168 for the Extended Yale-B database. Our experimental results showed that the proposed method improved face recognition accuracy by approximately 100% for the two databases used at a significantly reduced feature extraction time compared to the current state-of-art face recognition methods for all test cases. The SSAE approach can explore large and complex datasets with minimal computation time. In addition, the algorithm minimizes the false acceptance rate and improves recognition accuracy. This implies that the proposed method is promising and has the potential to enhance the performance of face recognition systems.
Author	Usman, Opeyemi Lateef Singh, Harprith Kaur Rajinder Jaber, Abdullah Ghanim Muniyandi, Ravie Chandren
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SubjectTerms	Accuracy Algorithms Bandwidths Datasets Deep learning deep neural network Efficiency face recognition Facial recognition technology Gabor filter Human subjects hybrid method Localization Methods Neural networks stacked sparse autoencoders
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Title	A Hybrid Method of Enhancing Accuracy of Facial Recognition System Using Gabor Filter and Stacked Sparse Autoencoders Deep Neural Network
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