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Journal Article
Face Recognition at a Distance for a Stand-Alone Access Control System
- Authors
- Hansung Lee, So-Hee Park, Jang-Hee Yoo, Se-Hoon Jung, Jun-Ho Huh
- Issue Date
- 2020-02
- Citation
- Sensors, v.20, no.3, pp.1-18
- ISSN
- 1424-8220
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Project Code
- 20HS2400, Development of AI Technology for Early Screening of Infant/Child Autism Spectrum Disorders based on Cognition of the Psychological Behavior and Respon, Yoo Jang-Hee
- Abstract
- 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.
- KSP Keywords
- Access control system, Active cooperation, Benchmark datasets, Computationally Efficient, Eyes detection, Face Image, Face dataset, False acceptance rate, Feature size, Gabor Wavelet, High accuracy
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
