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

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.

Additional Information Delivery to Image Content via Improved Unseen–Visible Watermarking

“…Considering Table 4 and several aspects of each algorithm shown in Table 5, the proposal is more suitable to be used in practical applications of additional information delivery, information security tasks [15][16][17][18][19] and other related fields [20], such as is mentioned in Section 1.…”

“…In this proposal, additional information delivery to the image content refers to the application of the general scenario of the UVW algorithms in daily life; when the endusers exhibit the QR watermark, the decoded information could be a company website, information of singers, dress designer, location or another kind of information that has or does not have relation to the image content. Additionally, it is not limited to extend the applicability to information security tasks such as medical imaging [15,16], deepfake prevention [17], 3D-video protection [18], face recognition authentication [19] and other related fields [20]. However, to obtain better results in all those applications, several drawbacks of the UVW algorithms must be improved: first, the algorithms only require large image regions with low intensity [6][7][8]14] second, embedding strategies increase histogram distortion and visual degradation of the watermarked image, impacting the legibility of the visual content and the watermark imperceptibility.…”

Distributed sensing units deploying on group unmanned vehicles

“…7 Using the libraries Python and OpenCV, face recognition can involve deep learning convolutional neural network (CNN) model and dynamic access control. 8 When the system recognizes a person’s face, a red square mark will appear around his or her head on the mobile phone panel to indicate the specified person.…”

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization

“…The obtained results showed a tremendous reduction in false recognition rate, and the model with liveness detection and face recognition was able to be deployed for identity authentication. Lee et al [20] constructed a lightweight and computationally efficient model to perform face recognition for a stand-alone access control system. The proposed model was based on the framework composed of the local binary pattern (LBP) and the AdaBoost classifier.…”