Biopsy method for use in human face identification

Abstract

The invention provides a biopsy method for use in human face identification and belongs to the technical field of mode identification. The algorithm provided by the invention comprises: firstly, giving a 'please-face-the-camera-with-face' prompt to a user logging in regardless of the current posture of the user, finding the face and eye socket areas by using an Adaboost human face sorter during the posture correction of the user, determining upper and low eyelids and left and right eye corners by using differential projection and precisely framing the positions of the eye sockets; secondly, computing optical flow field of two adjacent frames in an input video sequence by using an LK algorithm; and thirdly, further processing obtained optical flow data to obtain an optical flow amplitude, acquiring the number of the pixels with high amplitude, computing the specific gravity of the pixels with high amplitude, and determining eye movement if the proportion is big. Experiments show that for a real human face, eye movement can be detected easily due to a heavy optical flow generated on the eyes in a posture correction and eye twinkling process, while for a picture, eyes only perform a micro movement no matter how the picture moves or translations. When the method is used for biopsy, a better effect can be obtained.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a living body detection method based on misleading prompt information and optical flow field applied in face recognition. Background technique [0002] With the development of biometric technology, face recognition technology has become mature. Under good lighting conditions and postures, the face recognition system can already perform face detection and recognition well. However, for some As far as the face recognition technology of other systems is concerned, users can deceive the system with illegal means such as photos. For this type of system, the higher the recognition rate, the greater the security risks. Therefore, liveness detection has become a bottleneck for the application of face recognition technology in such systems. [0003] Liveness detection in traditional face recognition systems can be roughly divided into two methods: user cooperation and blin...

AI Technical Summary

Problems solved by technology

The advantage of the blind detection method is strong concealment and high security, but the disadvantage is that there are few living features that can be used stably and effectively

For example, eye blinking is a commonly used living feature in blind detection methods, but according to statistics, the average person blinks once every 2 to 4 seconds, which is too long, and there are many people who do not blink for a long time

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