Wang Ting
ABSTRACT
As the first two steps of face recognition system, face detection and key point location are of great significance to the accuracy of face recognition system. The Multi-task Cascaded Convolutional Network(MTCNN) uses a single network model for the first time to complete the task of face detection and key point location. It is easy to find the problem of false detection in the practice of people's face detection. Therefore, this paper introduces multi-scale and multi template data preprocessing in MTCNN model. By increasing the diversity of data, the accuracy of MTCNN model is enhanced. The results show that compared with the original MTCNN model, the recall of the hybrid test set composed of WIDER Face and Celeba is increased by 2% after introducing multi-scale and multi template data preprocessing.
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