A sparse representation-based local occlusion recognition method for athlete expressions

A sparse representation-based local occlusion recognition method for athlete expressions is proposed to address the problems of large mean square error, low recall rate, and poor recognition performance. We calculate the gradient direction and size of image pixels, divide image blocks, count the histogram of the gradient direction of the image blocks, combine all small histograms into a feature vector, and obtain the facial feature extraction results. The LBP algorithm is used for local occlusion image segmentation, and a sparse representation model is established to extract expression features. By dividing the image into blocks and solving the sparse representation coefficients of each block, local occlusion expression recognition is achieved. Experimental results show that the maximum mean squared error of the proposed method for facial expression recognition is only 0.21, and the maximum recall rate is more than 80%, which shows that it can effectively recognise occluded parts.