Face expression recognition method based on deep learning

Abstract

The present invention discloses a face expression recognition method based on deep learning. The method comprises the steps of: decomposing obtained video signals into image sequences, and establishing a skin color model to perform skin detection and remove a background area; employing a face detecting algorithm of a space skin color model to train a face classifier and perform face detection of candidate face areas to obtain a face area; performing processing of the face area image such as affine transformation and correction to obtain a face expression image; and finally, extracting LBP features from the expression image, employing a deep belief network to perform learning and training of the LBP features to obtain higher-level extraction features, and taking the higher-level extractionfeatures as a classifier for face expression recognition. The face expression recognition method based on deep learning can be widely applied at occasions such as public safety, psychological lie-detection and smart home, and a computer can provide more humanized service for the mankind through expression recognition. The face expression recognition method has very wide application scenes and verygood application prospects.

Description

Technical field:

[0001] The invention belongs to the technical field of intelligent graphic analysis, and in particular relates to a method for recognizing facial expressions based on deep learning. Background technique:

[0002] With the development of technology, facial expression recognition has gradually become a hot research direction. In the communication between people, the information conveyed by facial expressions occupies a considerable proportion. Facial expressions reflect the rich emotional activities in the inner world of human beings, and are important carriers of human behavior information and emotions. More in-depth research on facial expression recognition can help us better understand the true state of human inner emotions. As for computers, if it is possible to analyze and understand human facial expressions and obtain the emotions expressed by human faces through technical means, then computers can achieve better human-computer interaction and thus bec...

Examples

Embodiment Construction

[0068] Specific embodiments of the present invention are described in detail below, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0069] Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations thereof such as "includes" or "includes" and the like will be understood to include the stated elements or constituents, and not Other elements or other components are not excluded.

[0070] Firstly, the obtained video signal is decomposed into time-sorted image sequences, converted to the YCgCr color space, the skin color model is established for skin color detection, and the background area is removed after morphological processing to obtain the candidate skin color area; The Adaboost face detection algorithm of the color space skin color model trains a face classifier based on Haar-like features and performs face detection on the candidate skin color are...

Technical field:

[0001] The invention belongs to the technical field of intelligent graphic analysis, and in particular relates to a method for recognizing facial expressions based on deep learning. Background technique:

[0002] With the development of technology, facial expression recognition has gradually become a hot research direction. In the communication between people, the information conveyed by facial expressions occupies a considerable proportion. Facial expressions reflect the rich emotional activities in the inner world of human beings, and are important carriers of human behavior information and emotions. More in-depth research on facial expression recognition can help us better understand the true state of human inner emotions. As for computers, if it is possible to analyze and understand human facial expressions and obtain the emotions expressed by human faces through technical means, then computers can achieve better human-computer interaction and thus bec...

Examples

Embodiment Construction

[0068] Specific embodiments of the present invention are described in detail below, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0069] Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations thereof such as "includes" or "includes" and the like will be understood to include the stated elements or constituents, and not Other elements or other components are not excluded.

[0070] Firstly, the obtained video signal is decomposed into time-sorted image sequences, converted to the YCgCr color space, the skin color model is established for skin color detection, and the background area is removed after morphological processing to obtain the candidate skin color area; The Adaboost face detection algorithm of the color space skin color model trains a face classifier based on Haar-like features and performs face detection on the candidate skin color are...

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