Human face image preprocessing method for human face recognition

Abstract

The invention discloses a human face image preprocessing method for human face recognition. The method comprises: determining from the acquired image data the facial part image of a to-be-recognized target; performing recognition to the two pupils in the facial part image and determining the positions of the two pupils; according to the position relationship of the two pupils, correcting the facial part image as the front view for the facial part of the to-be-recognized target; and according to the front view, determining the sampling scope of each to-be-determined key characteristic point. According to the method of the invention, on the basis of the image preprocessing in the prior art such as grayscale correction and noise filtering, the step of determining the sampling scopes of key characteristic points is added wherein the facial part image is corrected as a front view and according to the position distribution of each key characteristic point on the facial part, preliminary scopes are divided. With the embodiments of the invention, the sampling scopes are narrowed so as to effectively increase the sampling efficiency for the subsequent extracting of key characteristic points.

Description

technical field [0001] The invention relates to biometric feature recognition, in particular to a face image preprocessing method in face recognition. Background technique [0002] Face recognition technology generally includes four components, which are face image acquisition, face image preprocessing, face image feature extraction, and matching and recognition. Specifically: [0003] Face image acquisition and detection refers to the acquisition of video or image data including human faces through video image acquisition devices such as camera lenses, which can be static images, dynamic images, different positions, and different expressions of the acquisition object. [0004] Face image preprocessing refers to determining the part of the face from the collected image data, and performing image preprocessing such as grayscale correction and noise filtering, so that the subsequent face image feature extraction process can be more accurate and efficient. [0005] In the prio...

AI Technical Summary

Problems solved by technology

[0006] After research, the inventor found that when the face image processed by the image preprocessing method in the prior art is extracted from the key feature points of the subsequent face image, the probability of key feature point positioning error is high, thus affecting the face image. Overall efficiency and effectiveness of recognition

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