Multiple attitude human face detection and track system and method

Abstract

This invention discloses one method and system for different human faces to test multiple faces in the test sequence for continuous tracing in the images, which comprises the following steps: separately getting human face front and side test mode through sample training to determining AAM human face mode; using the above modes to test the input visual image to determine whether one frame of image is stored in the human face, if testing the face and then tracing and validating the human face in the back frame.

Description

technical field [0001] The invention relates to a human face detection and tracking system and method thereof, in particular to a multi-pose human face detection and tracking method. Background technique [0002] Human face is one of the most convenient ways of human-computer interaction in computer vision system. Face detection is to determine the position, size and other information of all faces in an image or image sequence, while face tracking is to continuously track one or more detected faces in a video sequence. Face detection and tracking technology is not only a necessary prerequisite for face recognition, expression recognition, face synthesis and other technologies, but also has a wide range of application values ​​in intelligent human-computer interaction, video conferencing, intelligent monitoring, video retrieval and other fields. [0003] The image targeted by this system is the video sequence input by the video camera. Previously, the present applicant has ...

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Problems solved by technology

[0004] However, the algorithm in the above patent application also has certain limitations

First of all, the algorithm only trains the detection model of the frontal face, and cannot detect the profile face, which means that the detection and verification of this face can only be aimed at the frontal face, which greatly limits the application range of the algorithm; secondly, the The algorithm only tracks the face through the skin color histogram, and the skin color features of the face are very susceptible to interference from other skin color areas such as the neck, hands, or similar skin color areas such as yellow clothes. It is reflected in the tracking results that the tracking area sometimes jumps to the hands and neck or yellow clothes; again, the size and position of the tracking area obtained by the original algorithm change drastically, and even if the face remains still, the tracking result will vibrate; and the algorithm cannot obtain further details of the face. Posture information, such as the rotation angle of the face, the current general posture, etc.

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