吉林大学学报(信息科学版) ›› 2025, Vol. 43 ›› Issue (3): 598-604.

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基于孪生神经网络的智慧门禁人脸识别算法设计

李  炜1, 黄  倩2   

  1. 1. 武汉大学人民医院信息中心,武汉430060;2. 武昌职业学院 通识课部,武汉430060
  • 收稿日期:2023-07-03 出版日期:2025-06-19 发布日期:2025-06-19
  • 作者简介:李炜(1981— ), 男, 兰州人, 武汉大学人民医院工程师, 主要从事医院信息管理、数据库应用研究, (Tel)86- 18971095881(E-mail)gogo5467@126. com。
  • 基金资助:
    国家自然科学基金资助项目(62172171)

Design of Intelligent Access Control Face Recognition Algorithm Based on Twin Neural Network

LI Wei1, HUANG Qian2   

  1. 1. Information Center, Renmin Hospital of Wuhan University, Wuhan 430060, China; 2. General Education Department, Wuchang Polytechnic College, Wuhan 430060, China
  • Received:2023-07-03 Online:2025-06-19 Published:2025-06-19

摘要: 为提高智慧门禁人脸识别结果准确性和识别效率,从而提升智慧门禁系统的智慧化服务,提出一种基于 孪生神经网络的智慧门禁人脸识别算法。 对人脸图像信号进行小波变换获取小波系数,选择合适的阈值处理 小波系数,再次对小波系数进行逆变换,得到去噪后的人脸图像;并经去噪后,在孪生神经网络内将其输出值 映射处理,形成维数为128的特征向量;引入对比损失函数, 通过比较样本网络输出特征向量间的欧氏距离 判断其相似度,最终实现智慧门禁人脸识别。 实验结果表明,所提算法的智慧门禁人脸识别结果和识别效率 明显优于其他算法。

关键词: 孪生神经网络, 智慧门禁, 人脸识别, 图像去噪 

Abstract:  In order to improve the accuracy and efficiency of face recognition results of smart access control system, and thus enhance the intelligent service of smart door security, a smart access control face recognition algorithm based on twin neural network is proposed. The wavelet coefficients of the face image signal are obtained by wavelet transform, the appropriate threshold is selected to process the wavelet coefficients, and the inverse transform of the wavelet coefficients is carried out again to obtain the de-noised face image. After the face image is de-noised, the output value of the face image is mapped and processed in the twin neural network to form a feature vector with a dimension of 128. The contrast loss function is introduced to determine the similarity of the face image by comparing the Euclidean distance between the output feature vectors of the sample network, and finally realize intelligent access control face recognition. The experimental results show that the intelligent access control face recognition results and recognition efficiency of the proposed algorithm are significantly better than other algorithms. 

Key words: twin neural network, smart access control, face recognition, image denoising

中图分类号: 

  • TP391.4