Facial expression classification is widely used in various research fields. This paper putts forward to an algorithm based on deep learning to solve the problem. Firstly, it segments the facial region and transforms it to gray image, then constructs a convolutional neural network which includes convolution layers and max-pooling layers and full connection layers. The network can classify the facial expression to two categories which are happiness and sadness. Experiments are used to test and verify the network. And it has achieved good results
In passenger flow statistics, camera shoots from top to bottom. This paper put forward to a method,which calculates the disparity of targets region. Firstly, the left view is used to detect all possible head targets, and then the target and its surrounding pixels are selected as the matching points. According to some matching constraints, it is used to match with the right view, and the disparity of the target and its surrounding pixels is calculated. Taking the target and its surrounding pixels as a whole, it overcomes the disadvantage of weak texture in the head area, improves the matching accuracy and improves the accuracy of disparity calculation. According to the disparity threshold, the number of false heads is greatly reduced and the recognition accuracy is improved.
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