Face recognition method based on 4dlbp

Abstract

The invention relates to a human-face identification method based on 4DLBP (4-Dimnesional Local Binary Patterns). The human-face identification method based on 4DLBP comprises the following steps including image preprocessing, 4DLBP characteristic extraction and ELM (Extreme Learning Machine) training classification, wherein the ELM training classification comprises two stages: a training image set stage and a test image set stage, human-face images are respectively subjected to image preprocessing in each stage, subblock sub images of the images are subjected to 4DLBP characteristic extraction, characteristics obtained from each block of sub images are connected in series for obtaining the 4DLBP characteristics of the whole human face, then, the extracted human-face 4DLBP characteristics are utilized as the input of an ELM classifier for carrying on training and testing, finally, classes of the images are obtained, and the identifying is completed. The human-face identification method has the advantages that center point pixel values are added on the basis of 3DLBP (3-Dimensional Local Binary Patterns) for forming the 4DLBP characteristics, the human-face image local characteristics can be effectively extracted for improving the human-face identification performance.

Description

technical field [0001] The invention belongs to the field of biological feature recognition, in particular to a face recognition method based on 4DLBP. Background technique [0002] Face recognition technology is an important topic in biometric technology, and it is a very active research direction at present. Compared with using other biometrics for identification, face recognition has the advantages of directness, convenience, friendliness, and non-invasiveness, so it has extremely broad application prospects. However, there are also some difficulties in the development of face recognition. First of all, the face is a three-dimensional non-rigid irregular surface; secondly, the face will change with age, health and expression; thirdly, when collecting face images, different lighting and angles will affect face recognition ground accuracy. Since the mechanism of face recognition by the human brain is still unknown, robot face recognition is still in the stage of explorat...

AI Technical Summary

Problems solved by technology

However, the LBP operator regards feature extraction as a single process and does not make full use of the relationship between samples.

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