A face recognition hardware architecture based on lbp features

Abstract

The present invention relates to a face recognition hardware architecture based on LBP features, which includes: an LBP value calculation module; a block statistics module connected with the LBP value calculation module; a ping-pong cache structure connected with the block statistics module; And a comparison identification module connected with the ping-pong cache structure. The present invention calculates the LBP numerical value of each pixel data while continuously receiving the pixel data of the face detection grayscale pictures, and then performs histogram statistics on each LBP value while calculating the features of each stored face detection grayscale picture Read out the vector, and read out the stored feature vectors of each face detection gray-scale picture while continuously storing the feature vectors of each face detection gray-scale picture, and finally simultaneously store multiple face detection gray-scale pictures The high-degree pictures are compared with all training pictures, so as to achieve a substantial increase in the speed of face recognition.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a face recognition hardware architecture based on LBP features. Background technique [0002] Image processing is a very popular field recently, involving military, industry and all aspects of life. Face recognition, as a sub-field in the field of image processing, has also been widely used in the fields of identity recognition and verification, information security, etc., and has produced many commercial face recognition systems. The research on face recognition has never stopped in recent decades, which makes the face recognition system more and more perfect and mature. After decades of development, the face recognition system has developed into a mature process. According to the steps, the general process can be divided into three steps: face detection, feature extraction, and face recognition. [0003] The Local Binary Pattern (LBP) was proposed by TimoOjala et al. at the U...

AI Technical Summary

Problems solved by technology

However, this method has the following disadvantages: Firstly, saving all pixel information wastes hardware resources, and the subsequent steps must be saved after all pixel information is saved, so it does not conform to the pipeline implementation plan. There is a lot of room for improvement in parallelism

In addition, combined with the actual situation, in order to achieve better results in practical applications, face recognition algorithms often require a large number of training set samples and train them. At the same time, the number of test set pictures in practical applications is also very large, and a large number of training Samples and test samples will have a huge impact on recognition speed

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