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Facial expression recognition method based on convolutional long-short-term memory network

A facial expression recognition, long-term and short-term memory technology, applied in the field of image recognition, can solve problems such as different facial posture angles, facial area occlusion, affecting the accuracy of expression recognition, etc., to reduce dependencies and improve implementation efficiency.

Pending Publication Date: 2021-03-30
ZHEJIANG LAB
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Problems solved by technology

[0007] Studies have shown that in real natural scenes, there are still the following problems that affect the accuracy of expression recognition: 1. The angle of the face pose is different, 2. There are partial occlusions in the facial area

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  • Facial expression recognition method based on convolutional long-short-term memory network

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Embodiment Construction

[0039] In order to make the object, technical solution and technical effect of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic ideas of the present invention, and only the components related to the present invention are shown in the diagrams rather than the number, shape and shape of the components in actual implementation. Dimensional drawing, the type, quantity and proportion of each component can be changed arbitrarily during actual implementation, and the component layout type may also be more complicated.

[0040] Such as figure 1 As shown, a facial expression recognition method based on convolutional long short-term memory network, including the following steps:

[0041] Step 1. Detect the corresponding human face area from the expression imag...

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Abstract

The invention relates to the technical field of image recognition, particularly to a human face expression recognition method based on a convolutional long-short-term memory network. The method comprises the following steps: 1, detecting a corresponding human face area from a to-be-recognized expression image, and intercepting a human face area image block for expression recognition; 2, dividing the acquired face area into a plurality of face sub-area image blocks; 3, normalizing the human face sub-region image blocks to the same size; and 4, inputting the normalized face sub-region image blocks into a ConvLSTM model for feature fusion and feature classification. According to the method, the influence of the face posture on expression recognition can be effectively reduced, accumulated errors caused by step-by-step implementation of feature extraction and feature classification are reduced, and the expression recognition accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a facial expression recognition method based on a convolutional long-short-term memory network. Background technique [0002] Facial expressions play an important role in daily communication. Traditional human-computer interaction cannot understand and adapt to people's emotions and moods. Therefore, facial expression recognition is an important research field for establishing emotional communication between machines and humans. At present, expression recognition has been involved in the fields of intelligent tutoring systems, service robots, and driver fatigue detection. This technology has also received more and more attention in the field of computer vision. [0003] In related technologies, the expression recognition method can be divided into the following steps: [0004] (1) Face extraction, which detects the face area from the image to be recognized, which is u...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06N20/10G06N20/20
CPCG06N3/049G06N3/08G06N20/10G06N20/20G06V40/174G06V40/168G06V40/172G06V10/44G06N3/047G06N3/045G06F18/2411G06F18/214G06F18/2415G06F18/241
Inventor 李太豪刘昱龙廖龙飞裴冠雄
Owner ZHEJIANG LAB
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