Face expression identification method based on partially shielded image

Abstract

The invention discloses a face expression identification method based on a partially shielded image. The method comprises the following steps that 1) face images of N types of expressions included in a known type of face expression library are preprocessed; 2) the similarity between a sample to be tested in a test set and a training sample in a training set is calculated, and an image in the same type with the sample to be tested and nearest to the sample to be tested is obtained; 3) a shielded part of the sample to be tested is reconstructed; 4) PWLD features are extracted from the reconstructed sample to be tested and the training sample in the training set; and 4) an SVM classifier is used to identify all the samples to be tested in the test set in a classified manner. According to the invention, an image matching method is used to effectively reconstruct the shielded part of the image, the problem that features cannot be represented completely when only non-shielded parts are extracted is solved, a three-layer pyramid structure is used to extract global and local features of the image, and the accuracy of feature representation is improved.

Description

Technical field: [0001] The invention relates to image reconstruction and feature extraction, and belongs to the field of image processing, in particular to a facial expression recognition method based on partially occluded images. Background technique: [0002] Facial expression recognition has received extensive attention in human-computer interaction, intelligent information processing, etc., but most of the current research is carried out in a controlled environment, which is difficult to adapt to the complexity and variability of the external environment. Covered by glasses, scarves, masks and some random occluders, the recognition rate of facial expressions is greatly reduced. In recent years, research on facial expression recognition under occlusion has become an important research direction. Nowadays, some researchers dealing with facial expression recognition under occlusion try to reconstruct the texture and geometric features of the occlusion part, so as to elimi...

AI Technical Summary

Problems solved by technology

Although SpPCA overcomes the disadvantage that PCA does not distinguish the importance of different parts when expressing different expressions, for larger occlusions, the area of ​​the occlusion part may be divided into a smaller area separately, in this smaller area After calculating the eigenvalues ​​and eigenvectors, the reconstructed image still contains more occlusions, which will also cause the problem of low image recognition rate.

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