Facial expression recognition method and system based on regional grouping and internal association fusion
A facial expression recognition and facial expression technology, applied in the field of facial expression recognition, can solve problems such as poor recognition effect of facial expression recognition methods, and achieve the effect of improving accuracy
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Embodiment 1
[0037] The purpose of this embodiment is to provide a facial expression recognition method based on the fusion of region grouping and internal association.
[0038] In the real world, faces are subject to unpredictable occlusions under natural conditions. Facial occlusion can be caused by changes in head posture, lighting, masks, glasses, etc. For the frontal face with natural occlusion, the recognition effect of the unoccluded facial expression recognition method is poor. Therefore, facial expression recognition under unlimited occlusion is still a challenge.
[0039] In order to solve this problem, the present disclosure proposes an Interrelated Fusion CNN (IRF-CNN) to achieve the extraction and fusion of multi-semantic expression features of occluded face images. See the overall architecture of the network. figure 1 . IRF-CNN mainly includes three modules, namely Partial-occlusion Pre-processing module (POPM), Statistical Patches Grouping module (SPGM) and Interrelated R...
Embodiment 2
[0127] The purpose of this embodiment is to provide a facial expression recognition system based on regional grouping and internal association fusion.
[0128] A facial expression recognition system based on regional grouping and internal association fusion, including:
[0129] a data acquisition module, which is used to acquire the facial expression image to be recognized, and perform preprocessing;
[0130] A model building module, which is used to build a convolutional neural network model of intrinsic association fusion;
[0131] A facial expression recognition module, which is used to perform expression recognition on facial expression images by using a pre-trained convolutional neural network model, and output an expression recognition result;
[0132] Wherein, the convolutional neural network model includes an occlusion preprocessing module, a patch grouping module, and an intrinsic correlation reasoning fusion module, wherein the partial occlusion preprocessing module...
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