Face detecting and tracking method and device

Abstract

The invention provides a face detecting and tracking method and a device. The method comprises the steps of inputting a face image or a face video, preprocessing the face image or the face video in an illumination mode, detecting a face by usage of an Ada Boost algorithm, confirming an initial position of the face, and tracking the face by the usage of a Mean Shift algorithm. According to the face detecting and tracking method and the device, a self-adaptation local contrast enhancement method is provided to enhance image detail information in the period of image preprocessing, in order to increase robustness under different illumination conditions, face front samples under different illumination are added to training samples and accuracy of the face detection is increased by adoption of the Ada Boost algorithm in the period of face detection, in order to overcome the defect that using color of the Mean Shift algorithm is single, grads features and local binary pattern length between perpendiculars (LBP) vein features are integrated by adoption of the Mean Shift tracking algorithm in the period of face tracking, wherein the LBP vein features further considers using LBP local variance for expressing change of image contrast information, and accuracy of the face detection and the face tracking is improved.

Description

technical field [0001] The invention relates to the technical fields of pattern recognition and image processing, in particular to a face detection and tracking method and device. Background technique [0002] Face detection and tracking are two independent and inseparable problems. Face detection is the basis and premise of face tracking, and face tracking can be regarded as based on face detection, and the two are related to each other. In recent years, with the development of the network, face detection and tracking in video has broad application prospects, such as video conferencing, distance education, video surveillance, etc. [0003] The main purpose of face detection is to divide the input image into two parts: face area and non-face area. According to different detection principles, commonly used face detection methods are divided into: detection methods based on facial features and detection methods based on statistical models. Faces contain rich and diverse fea...

AI Technical Summary

Problems solved by technology

In the environment where the lighting cannot be controlled, such as outdoor sunlight, the face features are obviously affected by the direction and intensity of the light and produce nonlinear changes, making face detection difficult.

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