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Video facial expression early detection method based on multi-instance learning

A multi-instance learning, facial expression technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as the inability to realize real-time expression detection, and achieve the effect of improving accuracy and timeliness

Active Publication Date: 2018-05-15
SOUTHEAST UNIV
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Problems solved by technology

None of these algorithms can achieve true real-time expression detection

Method used

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  • Video facial expression early detection method based on multi-instance learning
  • Video facial expression early detection method based on multi-instance learning
  • Video facial expression early detection method based on multi-instance learning

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Embodiment Construction

[0056] This embodiment provides a method for pre-detection of video facial expressions MIEFD based on multi-instance learning, such as figure 1 shown, including the following steps:

[0057] (1) Preprocess the video data of the training sample and the sample to be tested, and extract the face area of ​​each frame image in the video. This step specifically includes:

[0058] (1-1) For the video data of the training sample and the sample to be tested, the Deep Convolutional Network Cascade proposed by Wang Xiaogang et al. in CVPR13 is used to extract 5 key points of the face in each frame of the video. Point position coordinates, including the two eyes, the tip of the nose and the left and right corners of the mouth.

[0059] Facial key point detection is very important for face analysis and recognition. The paper "Deep Convolutional Network Cascade for Facial Point Detection" published by Wang Xiaogang et al. on CVPR13 proposed a cascaded regression of a three-level convoluti...

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Abstract

The invention discloses a video facial expression early detection method based on multi-instance learning. The method comprises the steps that (1) video data of training samples and to-be-tested samples is preprocessed, and a facial region in each frame image in a video is extracted; (2) an LBP descriptor is adopted to perform feature extraction on the facial region, obtained through preprocessing, in each frame image, and feature vectors of each frame image are obtained; (3) an expression early detection function is solved by the adoption of an extended structured output support vector machine based on multi-instance learning according to the feature vectors of the training samples; and (4) the early detection function obtained in the step (3) is used to perform facial expression early detection according to the features vector of the to-be-tested samples in the step (2), and an expression early detection result is obtained. Through the method, expressions can be monitored in real time, and the recognition rate is high.

Description

technical field [0001] The invention relates to an expression detection method, in particular to a method for pre-detection of facial expression in video based on multi-instance learning. Background technique [0002] Expressions are an important way for humans to communicate emotional information and identify each other's attitude and inner world. Facial expression recognition technology is the basis for robots to understand human emotions, and provides technical support for research in the fields of efficient and intelligent human-computer interaction and multimedia information processing. . In recent years, the research on facial expression recognition based on video has received extensive attention from researchers at home and abroad. In 2016, "Jiajia", China's first interactive beauty robot developed by the University of Science and Technology of China, exhibited at the Summer Davos Forum was a wonderful appearance of facial expression recognition technology. The Chih...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/176G06N3/045G06F18/2413G06F18/2411
Inventor 谢利萍魏海坤张金霞郭伟立
Owner SOUTHEAST UNIV
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