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Design and implementation of a real-time face recognition system based on artificial intelligence techniques

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Abstract

This paper mainly discusses the asymmetric face recognition problem where the number of names in a name list and the number of faces in the photo might not be equal, but each face should be automatically labeled with a name. The motivation for this issue is that there had been many meetings in the past. After each meeting, the participant took group photos. The meeting provided only a corresponding name list of participants without one-to-one labels. In the worst case, the group photo might mix with the faces that were not participating in the meeting. Another reason for asymmetric face recognition is that some meeting personnel did not appear in photos because they assisted in taking pictures. This paper proposes an asymmetric face recognition mechanism, called AFRM in short. Initially, the proposed AFRM adopts the histogram of oriented gradients (HOG) and support vector machine (SVM) to detect and extract all faces from photos. Next, AFRM extracts the features from each face using the convolution feature map (Conv_FF) and adopts the features to partition the faces into different classes. Then, the AFRM applies the statistic-based mechanism to map each name in the name list to each face class. According to this mapping, each face will be associated with one name. To quickly identify a face during the meeting, the AFRM applies the K-nearest neighbors (KNN) to represent the features of each face. During the new meeting, the proposed AFRM can extract the feature of one face and then adopts KNN to derive the features. Experimental results show that the proposed mechanism achieves more than 97% accuracy without one-to-one name and face labeling.

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Data availability

The datasets generated during the current study are not publicly available but are available from the corresponding author.

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Funding

This study was not funded by any institution.

Author information

Authors and Affiliations

  1. Tamkang University, New Taipei, Taiwan

    Chih-Yung Chang, Shih-Jung Wu & Qiaoyun Zhang

  2. National Tsing Hua University, Hsinchu, Taiwan

    Arpita Samanta Santra

  3. National Taiwan Normal University, Taipei, Taiwan

    I-Hsiung Chang

  4. National Institute of Technology, Shillong, India

    Diptendu Sinha Roy

  5. Chuzhou University, Chuzhou, China

    Qiaoyun Zhang

Authors
  1. Chih-Yung Chang
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  2. Arpita Samanta Santra
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  3. I-Hsiung Chang
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  4. Shih-Jung Wu
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  5. Diptendu Sinha Roy
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  6. Qiaoyun Zhang
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All the authors have equally contributed, and all the authors have read and agreed to the published version of the manuscript.

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Correspondence to Chih-Yung Chang.

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This study does not involve either human subjects or animals.

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Communicated by R. Huang.

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Chang, CY., Santra, A.S., Chang, IH. et al. Design and implementation of a real-time face recognition system based on artificial intelligence techniques. Multimedia Systems 30, 114 (2024). https://doi.org/10.1007/s00530-024-01306-y

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