Human face recognition method and human face recognition system

Abstract

The invention provides a human face recognition method and a human face recognition system. The method comprises steps: features of all control points of a to-be-recognized image are extracted; with the control points as bifurcation points, features of a preset number of control points are extracted to form a decision tree with a preset depth; features of the decision tree are classified, and multiple human face rectangular images in the to-be-recognized image are acquired; the obtained multiple human face rectangular images are further zoomed to a uniformed size, and grey-scale images for the human face rectangular images are obtained; the grey-scale images for the human face rectangular images are inputted to a DCNN network well trained in advance, and human face features with preset dimensions are extracted; and comparison and recognition are carried out on the obtained human face features and human face data in a preset database. The problem of insufficient computing resources when the traditional high-precision human face recognition technology is applied to a mobile platform can be solved, smooth operation can still be realized in a condition of a low-frequency CPU and a small memory of an embedded system, and the human face recognition precision can be ensured.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a face recognition method and system. Background technique [0002] The research on face recognition technology began in the 1960s, and was improved with the development of computer technology and optical imaging technology after the 1980s, but it really entered the primary application stage in the late 1990s, and the United States, Germany and Japan technology implementation. The key to the success of face recognition technology lies in whether it has cutting-edge core technology to make the recognition result have a practical recognition rate and recognition speed. [0003] However, the current face recognition technology has the following defects: 1. It is sensitive to changes in the face area such as illumination, angle, expression and accessories, resulting in a high recognition rate in an ideal environment, but a poor recognition rate in actual application scenar...

AI Technical Summary

Problems solved by technology

[0003] However, the current face recognition technology has the following defects: 1. It is sensitive to changes in the face area such as illumination, angle, expression and accessories, resulting in a high recognition rate in an ideal environment, but a poor recognition rate in actual application scenarios; 2. The recent DCNN-based face recognition method has high theoretical accuracy, but it cannot run well on mobile platforms with limited computing resources, either because the speed is too slow or the memory is insufficient

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