An Improved Multi Task Cascaded Convolutional Neural Network Face Detection Algorithm
Face detection method based on convolutional neural network has high detection accuracy and wide application,but the convolutional neural network has a complex structure,large amount of calculation,and low effectiveness when running on edge computing devices.In order to improve the real-time detection,an improved multi-task convolutional neural network face detection algorithm is proposed.The detection method is realized based on MTCNN,using MobileNet to replace most of the convolution kernels in the three layer sub-network of MTCNN,and reducing the number of convolution kernels in R-Net and O-Net net works;improve the NMS algorithm,reduce the confidence of the suppressed candidate box,delete it uniformly at the last time,and suppress other candidate boxes except the highest score candidate box around the same face.When detected on edge computing devices,Celeb A and WIDER FACE datasets are selected for train ing and verification according to the 7:3 division,with an accuracy of 98.03%and good real-time perfor mance.The improved strategy greatly reduces the amount of parameters of the original network,realizes lightweight improvement,retains the regression window with higher accuracy,alleviates the missed detection problem,has a high detection rate,and can meet the demand of real-time face detection on edge computing devices.
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