A Glasses Removal Method for Fine-grained Face Recognition

Abstract

The invention belongs to the technical field of face recognition, and in particular relates to a method for removing glasses for fine-grained face recognition, comprising the steps of: dividing an initial face image wearing glasses into three image blocks, respectively using Part1 and Part2 and Part3 logo, where Part2 contains the complete glasses part; build the ERCNN network model of the glasses removal depth convolutional neural network, use Part2 as the input of the convolution layer of the ERCNN network model, perform feature selection and maximum element operation through the MFM unit in the network, and then Using the operation of deconvolution, average pooling and element-by-element weighted summation, Part2 is reconstructed, and then the new image block Part2_new after removing the glasses is obtained; the output Part2_new is merged with the original Part1 and Part3 to obtain a complete Face images with glasses removed.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a method for removing glasses for fine-grained face recognition. Background technique [0002] Face recognition is one of the most active research directions in pattern recognition and computer vision. As a common face occluder, glasses greatly affect the accuracy of face recognition, especially face recognition with fine-grained features such as similar faces. Currently, there are methods based on PCA or deep learning for removing glasses from face images. Among them, PCA is a more commonly used data analysis method. Its main idea is to calculate the principal component components, that is, the transformation matrix, according to statistical principles, so as to reconstruct the original vector. Although the PCA method can remove glasses from face images with glasses, it is susceptible to noise interference, and the removal effect is not ideal, so it cannot...

AI Technical Summary

Problems solved by technology

Compared with the technical solution of this proposal, this technical solution has the following disadvantages: 1. "Application No. 201711361308.9" adopts a symmetrical structure of N convolutional layers and N deconvolutional layers, and the number of network layers is large, so it is not easy to implement Training, and the trained model parameters and calculations are large

2. The glasses removal method proposed by "Application No. 201711361308.9" only applies it to general face recognition methods, and does not apply the reconstructed glasses-free face image to fine-grained face recognition, and does not define the effect on the reconstructed image. Quality Evaluation Criteria

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