Abstract
Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed. Firstly, the expression image is normalized by illumination based on the improved Weber face, and the key points of the face are detected by the Gaussian process regression tree. Then, the 3dmms model is introduced. The 3D face shape, which is consistent with the face in the image, is provided by iterative estimation so as to further improve the image quality of face pose standardization. In this paper, we consider that the convolution features used in facial expression recognition need to be trained from the beginning and add as many different samples as possible in the training process. Finally, this paper attempts to combine the traditional deep learning technology with capsule configuration, adds an attention layer after the primary capsule layer in the capsule network, and proposes an improved capsule structure model suitable for expression recognition. The experimental results on JAFFE and BU-3DFE datasets show that the recognition rate can reach 96.66% and 80.64%, respectively.
Highlights
Human facial expression is a kind of representation language which is naturally or deliberately revealed by the complex stimulation of environment, context, and mood in the process of communication and can be perceived by the visual system [1,2,3]
Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed. e improved capsule model can effectively classify facial expressions under unconstrained conditions, which makes up for the deficiency of pure deep convolution network in acquiring sparse features hidden in discriminative texture, and improves the generalization ability of existing expression classification models for illumination and pose differences
To verify the effectiveness of the proposed facial expression recognition method using CNN and improved capsule network model, the experimental evaluation was performed on the BU-3DFE and the JAFFE dataset. e proposed algorithm is compared with that proposed in reference [23], reference [15], and reference [19] through experiments
Summary
Human facial expression is a kind of representation language which is naturally or deliberately revealed by the complex stimulation of environment, context, and mood in the process of communication and can be perceived by the visual system [1,2,3]. Because the human facial expression is rich in psychological and emotional information, has stress convergence, and is controlled by consciousness, it is easy to get the general attention of a large number of scholars in the field of psychology and pattern recognition [7, 8]. Scientific Programming vision studies the difficulties faced by the traditional pattern recognition methods in image data or processes and refines the image data to facilitate the indexing, classification, and automatic analysis of image data [10]. E attention module uses sigmoid as an activation function, which can select important features and suppress irrelevant information It can help smooth the mismatch between the training set and test set and improve the final recognition rate
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