基于Gabor小波-卷积神经网络的在线考试场景模糊人脸识别方法

何剑萍1; 蒋大锐2; 杨波2

基于Gabor小波-卷积神经网络的在线考试场景模糊人脸识别方法

引用本文：何剑萍1，蒋大锐2，杨波2.基于Gabor小波-卷积神经网络的在线考试场景模糊人脸识别方法[J].计算技术与自动化,2026,(1):114-120

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作者	单位
何剑萍1，蒋大锐2，杨波2	（1.广州华商学院马克思主义学院,广东广州 511300；2.广州华商学院数据科学学院,广东广州 511300）

中文摘要:针对目前在线考试智能监考时因图像运动模糊、噪声等导致人脸识别准确率低的问题，提出了一种基于Gabor小波-卷积神经网络-支持向量机的人脸识别模型。将原始图像经GW滤波分解为包含了幅度和角度特征的协方差矩阵，从而提高模糊环境下人脸识别性能。还提出了一种改进的CNN网络学习GW生成的协方差矩阵，从而提取出人脸特征。应用SVM对人脸特征表示进行分类，最终输出人脸识别结果。通过实验验证，与PCANet、VGGFace、ResNet50模型相比，所提GW-CNN-SVM模型在低分辨率、运动模糊和噪声环境下识别性能更优。实验结果验证了所提GW-CNN-SVM模型对在线考试智能监考时低分辨率、运动模糊和噪声环境下的人脸识别具有更高的鲁棒性。

中文关键词:智慧教育在线监考人脸识别特征提取卷积神经网络协方差矩阵

Fuzzy Face Recognition Method in Online Examination Scene Based on Gabor Wavelet Convolutional Neural Network

Abstract:In order to solve the problem of low accuracy of face recognition due to image motion blur and noise in intelligent invigilation of electronic assessment, a face recognition model based on Gabor wavelet convolutional neural network support vector machine is proposed. The original image is decomposed into a covariance matrix containing amplitude and angle features by GW filtering, thus improving the performance of face recognition in fuzzy environments. An improved CNN network learning the covariance matrix generated by GW is proposed to extract the face features. Apply SVM to classify facial feature representations and ultimately output facial recognition results. Through experimental verification, compared with PCANet, VGGFace, and ResNet50 models, the proposed GW-CNN-SVM model has better recognition performance in low resolution, motion blur, and noisy environments. The experimental results verify that the proposed GW-CNN-SVM model has higher robustness in low resolution, motion blur, and noisy environments during intelligent invigilation of school exams.

keywords:smart education online invigilation face recognition feature extraction convolutional neural network covariance matrix

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