Abstract
Due to the variety of poses, lighting, and scenes, dynamic face detection and calibration pose a big challenge under unconstrained environment. In this paper, we use the inherent correlation between detection and calibration to enhance their performance in a deep multi-task cascaded convolutional neural network (MTCNN). In addition, we utilize Google’s FaceNet framework to learn a mapping from face images to a compact Euclidean space, where distances directly correspond to a measure of face similarity to extract the performance of facial feature algorithms. In the practical application scenario, we set up a multi-camera real-time monitoring system to perform face matching and recognition of collected continuous frames from different angles in real time.
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Jiang, B., Ren, Q., Dai, F., Xiong, J., Yang, J., Gui, G. (2020). Multi-task Cascaded Convolutional Neural Networks for Real-Time Dynamic Face Recognition Method. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_8
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DOI: https://doi.org/10.1007/978-981-13-6508-9_8
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