Eye positioning method applied to face recognition

Abstract

The invention relates to an eye positioning method applied to face recognition. The method includes the following steps that: a light reflecting area of an image is detected and removed; a face is detected with the Viola-Jones method of the Ada-Boost algorithm; the normalized gradient vector of a face area is calculated, binarization is carried out, a black frame of eyes is detected, the average gray value of adjacent pixels which are not located in a black frame area is adopted to replace the gray value of pixels which are in the black frame area; an eye training set and a non-eye training set are constructed, the non-linear SVM (Support Vector Machine) of a quadratic kernel function is trained, calculation and evaluation are carried out on areas which adopt pixels around the eyes as centers, a pixel with the maximum value is evaluated and is adopted as an eye position, the evaluation is named as confidence; and if the confidence is greater than a set threshold value, the eye position is a final positioning result, otherwise PCA (Principal Component Analysis) is adopted to evaluate the eye position, rotation and zooming transformation is performed on the face area, the Gabor coefficients of transformed images are calculated, and the confidence of face detection is calculated, an image with the largest confidence is selected, and the average position of eyes in the image is adopted as the eye position of the original image.

Description

technical field [0001] The invention relates to an eye positioning method in face recognition. Background technique [0002] Face recognition using face images for identity authentication is a very natural and convenient biometric recognition technology. The change of illumination is the most important factor affecting the performance of face recognition system. In order to avoid the influence of light, near-infrared images have been widely used in face recognition. Usually, the face recognition process includes face detection, eye location, preprocessing, feature extraction and comparison. Robust eye localization plays a very important role in face recognition systems. [0003] Traditional eye localization methods can be roughly divided into three categories: template-based methods, appearance-based methods, and geometric feature-based methods. [0004] Geometric feature-based methods localize eye locations based on eye properties such as edges and iris intensity. Thes...

AI Technical Summary

Problems solved by technology

This method can accurately detect eyes, but the detection effect on faces wearing glasses is unstable

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