Abstract
Face pose analysis has a very broad application prospect in the fields of public safety monitoring, human-computer interaction. Traditional deep learning methods are mostly based on public dataset training, and the robustness is poor in specific application scenarios. Secondly, most models need to crop the facial region during analysis, which is not only slow but also loses facial context in the natural environment. In response to these problems, this paper proposes a joint learning network model for Mobile Face Safe Detection and pose analysis. This method first proposes a cloud-service assisted semi-automated image annotation method. The image of the driver's pose in road traffic monitoring scenes is marked for, which provides additional training data for subsequent joint learning. Secondly, through the cascaded multi-task network, the problem of face pose analysis relying on Mobile Face Safe Detection is solved. At the same time, the fusion loss function, classified training data and Online Hard Example Mining (OHEM) training strategies are used to improve the robustness of the model in complex environments. In the end, the FDDB, AFLW and Prima data sets are used to verify the superiority of our model by comparing with other algorithms.
Highlights
With the current increase in computer computing speed, especially the speed of parallel floating-point operations, Computer vision technology based on deep learning convolutional neural network has made great progress [1], especially in the face recognition and analysis technology
Since Hiton and his student Alex used AlexNet [2] in 2012 to get the first place in ILSVRC, convolutional neural networks began to be widely used in computer vision, far exceeding the accuracy of other traditional methods
A rectangle and sent to the final layer convolutional neural network to obtain the results of Mobile Face Safe Detection and facial pose analysis
Summary
With the current increase in computer computing speed, especially the speed of parallel floating-point operations, Computer vision technology based on deep learning convolutional neural network has made great progress [1], especially in the face recognition and analysis technology. This paper proposes a fusion learning network model for Mobile Face Safe Detection and attitude analysis, which has two characteristics: a) This paper proposes a method for data set integration and labeling. The basic idea of the method is to preprocess the original image by using the existing commercial Mobile Face Safe Detection cloud services and the advanced deep learning neural network algorithm.
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