Human face emotion recognition method in complex environment

A technology for emotion recognition and complex environments, applied in the field of face recognition, can solve problems that are difficult to industrialize, and achieve high efficiency, reliability, and high accuracy

Inactive Publication Date: 2017-12-01
深圳帕罗人工智能科技有限公司
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the face emotion recognition method developed by any single technology above is difficult to evolve in

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  • Human face emotion recognition method in complex environment
  • Human face emotion recognition method in complex environment
  • Human face emotion recognition method in complex environment

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Embodiment Construction

[0022] In order to express the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings.

[0023] figure 1 As shown, it is the overall flow chart of the face emotion recognition method based on the complex environment realized by the present invention. The flow of face expression recognition is as follows: first input video pictures, then perform face detection, and preprocess the detection results , to remove interference (de-interference processing), and then perform line edge mapping for face area recognition and face area selection, and perform Gabor wavelet transformation on 8 regions of interest for the mapped data, and uniformly transform the transformed image The LBP feature extraction of the pattern, and then through the PCA principal component analysis to reduce the dimensionality of the extracted features, compare the feature vectors, compare the motor unit and the neutral expression, and fin...

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Abstract

The invention discloses a human face emotion recognition method in a mobile embedded complex environment. In the method, a human face is divided into main areas of a forehead, eyebrows and eyes, cheeks, a noise, a month and a chain, and 68 feature points are further divided. In view of the above feature points, in order to realize the human face emotion recognition rate, the accuracy and the reliability in various environments, a human face and expression feature classification method is used in a normal condition, a Faster R-CNN face area convolution neural network-based method is used in conditions of light, reflection and shadow, a method of combining a Bayes Network, a Markoff chain and variational reasoning is used in complex conditions of motion, jitter, shaking, and movement, and a method of combining a deep convolution neural network, a super-resolution generative adversarial network (SRGANs), reinforcement learning, a backpropagation algorithm and a dropout algorithm is used in conditions of incomplete human face display, a multi-human face environment and noisy background. Thus, the human face expression recognition effects, the accuracy and the reliability can be promoted effectively.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence mobile video image recognition, in particular to a face recognition method in a complex environment, which is an emerging field in the world at present. Background technique [0002] Face recognition based on video images is an emerging important research field in the world. Since NVIDIA launched the deep learning graphics processor graphics card (GPU) based on servers and computer hosts in 2006, the world's leading high-tech companies Such as Google, Apple, Amazon, well-known scientific research institutes such as MIT, Berkeley and Stanford, have adopted Nvidia's GPU graphics card to develop image recognition-based applications. In contrast, the global mobile embedded video image recognition market lags far behind similar server-based applications. It was not until mid-May 2017 that manufacturers including Nvidia began to provide high-performance machine vision and deep learning G...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06T3/40G06T3/60G06T7/60
CPCG06N3/084G06T3/40G06T3/60G06T7/60G06T2207/20081G06V40/171G06V40/174G06N3/045G06F18/2411
Inventor 陈援东陈忠
Owner 深圳帕罗人工智能科技有限公司
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