A facial expression recognition feature extraction algorithm based on the Weber multi-direction descriptor

Abstract

The invention relates to a facial expression recognition feature extraction algorithm based on the Weber multi-direction descriptor. The algorithm mainly comprises the steps of: performing Gabor wavelet transform on a facial expression image and fusing Gabor features in all directions of the same scale; dividing a Gabor feature image into non-overlapping sub-blocks and building graph structures inthe horizontal direction, in the vertical direction and in the directions of two diagonals; calculating the features values of the graph structures in the direction of 0 degree, 45 degrees, 90 degrees and 135 degrees, wherein the highest one in the four feature values is used as the differential excitation of the Weber multi-direction descriptor; calculating the gradients of a central pixel in two mutually vertical directions, wherein the direction of the bigger gradient in the two gradients is used as the main direction of the Weber multi-direction descriptor. The algorithm is reasonable indesign; the algorithm can extract more effective and identifiable texture detail features, thereby remarkably improving the facial expression recognition rate; the algorithm has great recognition stability and generalization ability and can be widely applied to image processing fields such as facial expression recognition.

Description

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AI Technical Summary

Benefits of technology

This technology helps extract important attributes from images like faces or other objects with unique appearance patterns through various techniques called ghost detection methods. By combining specific properties together it makes them easier for computers to recognize complex human movements without being affected by background noise. These advanced technologies help make things look smoother and clearer during video recording compared to traditional cameras.

Problems solved by technology

There exist various technical problem addressed in this patents relating to improving facial emotion detection techniques through neural networks. These solutions require accurate input data representation and computation capabilities, leading to slow processing times and reduced recognization rates compared to traditional approaches like histogram analysis and Fourier Transform. Therefore there exists needs for improvement over current technologies involving facial appearance measurement technology alone.

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