Method and system for identifying faces shaded by eyeglasses

Abstract

The invention discloses a method for recognizing faces shaded by eyeglasses, which comprises the following steps of: inputting an image of an eyeglass-free face and a general face model to acquire corresponding points on a face image and the face model, and performing coordinate transformation on all peaks to adjust the peaks under the same coordinate system; processing the model to acquire a facade head model of which the posture is corrected; adding an eyeglass model on the head model and pasting textures; performing fuzzy processing and light reflecting processing on the lenses of the eyeglasses; and generating virtual samples in which the eyeglasses are worn under different conditions by using a projection model. The invention also provides a corresponding face recognition system. The method and the system have the advantages of wide simulation range, high generality, greatly improved recognition effect, real-time property, high practicability, high recognition rate and the like, can comprise more changes of the eyeglasses, are quick and are easy to operate.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a masked face recognition method and system. Background technique [0002] As an important identification technology, face recognition has broad application prospects in security, finance, human-computer interaction, information, education and many other fields. The current face recognition system performs face recognition by comparing the template image in the template library with the recognition image formed by the face to be recognized, and has a good recognition effect when the acquisition conditions of the template image and the recognition image are basically the same. However, when there are occluders in the recognition image and the template image is not occluded, the recognition rate will be reduced due to the difference in features between the images. Among them, the occlusion of the glasses is particularly prominent, due to the texture, shape, position, ...

AI Technical Summary

Problems solved by technology

Among them, various glasses detection methods are applied, such as active shape model, snake model, artificial neural network (Artificial Neural Networks), Hough transform and other methods, but the problem with these methods is that they rely too much on The result of glasses detection, and due to the diversity of glasses, it is difficult to meet the requirements of glasses detection itself; the reconstructed image is distorted, and the reconstructed image is composed of training samples linearly, which must be different from the original image; for blurred or reflective lenses and frames The performance of darker colors is poor. There will always be leftovers such as frames and black spots in the reconstructed image. Specific glasses-wearing and corresponding non-glasses samples are required for learning.

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