Sequential Sample Consensus: A Robust Algorithm for Video-Based Face Recognition

Ding, Sihao; Li, Ying; Zhu, Junda; Zheng, Yuan F.; Xuan, Dong

doi:10.1109/tcsvt.2014.2351094

Cited by 8 publications

(1 citation statement)

References 37 publications

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“…The algorithm consists of three steps. In the first step, multiple cameras are used and successive frames are obtained from different angles; the candidate form is retained using a multi-task cascaded convolutional neural network (MTCNN) [18] [19] and the face in the video stream [20] [21] is extracted. In the second step, the FaceNet [22] [23] extraction is used.…”

Section: Related Workmentioning

confidence: 99%

Parallel Multi-task Cascade Convolution Neural Network Optimization Algorithm for Real-time Dynamic Face Recognition

2020

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Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

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