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Author

Jinbi Liang

Bio: Jinbi Liang is an academic researcher. The author has contributed to research in topics: Matching (statistics) & Facial recognition system. The author has an hindex of 1, co-authored 2 publications receiving 124 citations.

Papers
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TL;DR: A multi-granularity masked face recognition model is developed that achieves 95% accuracy, exceeding the results reported by the industry and is currently the world's largest real-world masked face dataset.
Abstract: In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the results reported by the industry. Our datasets are available at: this https URL.

277 citations

Patent
24 Apr 2020
TL;DR: In this paper, a space-time relationship matching association identification method for masked and reloading camouflage identities was proposed, and the method comprises the steps: firstly calculating apre-case relationship matching probability through employing case occurrence time, target disappearance time and the passing duration of a target from a disappearance position to a case occurrence point, and measuring the space time relationship matching degree.
Abstract: The invention discloses a space-time relationship matching association identification method for masked and reloading camouflage identities, and the method comprises the steps: firstly calculating apre-case space-time relationship matching probability through employing case occurrence time, target disappearance time and the passing duration of a target from a disappearance position to a case occurrence point, and measuring the space-time relationship matching degree of the target disappearance time and case occurrence time; calculating a post-case space-time relationship matching probabilityby utilizing the target reproduction time, the case termination time and the passing duration of the target from the case occurrence point to the reproduction position, and measuring the space-time relationship matching degree of the target reproduction time and the case termination time; calculating a joint matching probability according to the two spatial-temporal relationship matching probabilities, and measuring the spatial-temporal relationship matching degree of the pedestrian disappearing track and the case; and finally discovering a camouflage target and identifying the identity basedon the joint matching probability. According to the method, the thought of spatial-temporal behavior association is introduced, and the problem that morphology camouflage pedestrians cannot be associated by independently depending on visual attributes is solved.

Cited by
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Journal ArticleDOI
TL;DR: A hybrid model using deep and classical machine learning for face mask detection will be presented, and the SVM classifier achieved 99.64 % testing accuracy in RMFD.

540 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an approach using deep learning, TensorFlow, Keras, and OpenCV to detect face masks using Single Shot Multibox Detector as a face detector and MobilenetV2 architecture as a framework for the classifier.

193 citations

Journal ArticleDOI
TL;DR: This work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks and their combination for the global masked face detection (MaskedFace-Net).

156 citations

Journal ArticleDOI
TL;DR: The history of face recognition technology, the current state-of-the-art methodologies, and future directions are presented, specifically on the most recent databases, 2D and 3D face recognition methods.
Abstract: Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

155 citations

Journal ArticleDOI
08 May 2021-Sensors
TL;DR: In this article, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model.
Abstract: To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.

111 citations