Dynamic face identification system and method

Abstract

The present invention relates to a dynamic face identification system and method. The system comprises a detection module, a pre-processing module, a feature extraction training module, a feature extraction identification module. The detection module loads a face detector, reads a video stream or a panoramic image to detect the faces and displays, intercepts and saves the detected face images real-timely, the pre-processing module carries out the grey level transformation and the image normalization on the face images, detects by the accumulation of pixel values difference in wavelet domain (APVD) and retains the face images reaching a standard, the feature extraction training module establishes a training sample base and the indexes of the training samples, reads the face images reaching the standard to extract the features, and carries out the PCA feature dimensionality reduction and the BP neural network training, and the feature extraction identification module reads the face images reaching the standard, carries out the normalization, the curve Gabor wavelet (CGW) feature extraction and the PCA dimensionality reduction and identification, and outputs an identification result. According to the present invention, by calculating the fuzzy degree of the images, selecting the face images reaching the identification requirements, and using a curve Gabor wavelet to extract the effective face features, the identification rate is improved.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a dynamic face recognition system and method. Background technique [0002] As one of the most popular research topics in the field of computer vision today, face recognition technology has a wide range of applications in the security industry, access control systems, attendance systems, and human-computer interaction. The research on face recognition has a history of several decades, and has achieved fruitful results so far. However, most of the existing research work is aimed at static standard face images, and there are few studies on dynamic face recognition in actual scenes, which is still difficult to reach. application requirements. Compared with face recognition under static conditions, dynamic face recognition in actual scenes needs to consider more issues: the camera is out of focus or relative motion can easily cause image blur, and it needs to be considered ...

AI Technical Summary

Problems solved by technology

The research on face recognition has a history of several decades, and has achieved fruitful results so far. However, most of the existing research work is aimed at static standard face images, and there are few studies on dynamic face recognition in actual scenes, which is still difficult to reach. application requirements

Compared with face recognition under static conditions, dynamic face recognition in actual scenes needs to consider more issues: the camera is out of focus or relative motion can easily cause image blur, and it needs to be considered to exclude blurred images; it is difficult under video streaming conditions. To ensure that the collected face images are of high quality, how to extract useful information as much as possible is a challenge

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