Vivo-face detection method based on HSV (hue, saturation, value) color space statistical characteristics

Abstract

The invention discloses a vivo-face detection method based on HSV (hue, saturation, value) color space statistical characteristics. The method includes firstly, converting a face image captured by a camera from RGB (red green blue) color space to YCrCb color space; secondly, subjecting the face image to skin color segmentation, denoising, mathematical morphological treatment and connected region boundary calibration so as to obtain coordinates of a face matrix region; thirdly, acquiring a facial image to be detected from the face image according to the coordinates of the face matrix region; fourthly, segmenting the facial image to be detected into image blocks, and acquiring characteristic values of three color components of each image block in the facial image to be detected; and fifthly, using normalized characteristic values as a sample to be detected, and sending the sample to a trained support vector machine for detecting whether the face image is a vivo real facial image or not. The vivo-face detection method based on HSV color space statistical characteristics has the advantages that delay of a face recognition system is reduced, computational complexity is lowered, and detection accuracy is improved.

Description

technical field [0001] The invention relates to a living real human face discrimination technology applied to human face recognition, in particular to a living human face detection method based on statistical features of HSV color space. Background technique [0002] With the rapid development of biometric identification technology, face recognition, fingerprint recognition, iris recognition technology, etc. play an important role in identity verification, among which face recognition technology is the most convenient and most suitable for people's habits. has been widely applied. Face recognition can replace traditional passwords, and has more convenience than traditional passwords. You don’t have to worry about passwords being forgotten or deciphered by people with ulterior motives. Therefore, it has achieved rapid development in recent decades. At present, face recognition technology has been widely used in criminal investigation, banking system, social welfare, customs ...

AI Technical Summary

Problems solved by technology

There are mainly two existing liveness detection methods used in face recognition. The first detection method realizes detection by judging whether the face has physiological activities (such as blinking, lip movement, etc.); Fourier spectrum components are used for detection. These detection methods need to collect multiple photos, and the detection effect on low-resolution photos is poor; on the other hand, it will also increase the delay and computational complexity of the face authentication system, which will cause System resource consumption, so these detection methods are not ideal for ordinary small devices

At present, there is still a thermal infrared imaging analysis method, but this method needs to add expensive additional equipment, and the method itself has unstable factors, so it cannot be widely applied on ordinary equipment

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