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Open AccessArticle

Face Recognition at a Distance for a Stand-Alone Access Control System

1
School of Computer Engineering, Youngsan University, 288 Junam-Ro, Yangsan, Gyeongnam 50510, Korea
2
Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
3
Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
4
School of Major Connection (Bigdata Convergence), Youngsan University, 288 Junam-Ro, Yangsan, Gyeongnam 50510, Korea
5
Department of Data Informatics, Korea Maritime and Ocean University, Busan 49112, Korea
*
Authors to whom correspondence should be addressed.
This paper is an extended version of a conference paper published in Yoo, J.H.; Park, S.H.; Lee, Y.J. Real-Time Age and Gender Estimation from Face Images. In Proceedings of the 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017), Sydney, Australia, 20–22 November 2017.
Sensors 2020, 20(3), 785; https://doi.org/10.3390/s20030785
Received: 6 December 2019 / Revised: 20 January 2020 / Accepted: 22 January 2020 / Published: 31 January 2020
(This article belongs to the Special Issue Visual Sensor Networks for Object Detection and Tracking)
Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment. View Full-Text
Keywords: artificial intelligence; access control; face identification; face recognition at distance; face biometric; face recognition artificial intelligence; access control; face identification; face recognition at distance; face biometric; face recognition
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Lee, H.; Park, S.-H.; Yoo, J.-H.; Jung, S.-H.; Huh, J.-H. Face Recognition at a Distance for a Stand-Alone Access Control System. Sensors 2020, 20, 785.

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