한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제25권10호
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  • Pages.1375-1385
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  • 2022
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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단일 훈련 샘플만을 활용하는 준-지도학습 심층 도메인 적응 기반 얼굴인식 기술 개발

Development of Semi-Supervised Deep Domain Adaptation Based Face Recognition Using Only a Single Training Sample

  • Kim, Kyeong Tae (Division of Computer Engineering, Hankuk University of Foreign Studies) ;
  • Choi, Jae Young (Division of Computer Engineering, Hankuk University of Foreign Studies)
  • 투고 : 2022.06.27
  • 심사 : 2022.09.14
  • 발행 : 2022.10.31
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초록

In this paper, we propose a semi-supervised domain adaptation solution to deal with practical face recognition (FR) scenarios where a single face image for each target identity (to be recognized) is only available in the training phase. Main goal of the proposed method is to reduce the discrepancy between the target and the source domain face images, which ultimately improves FR performances. The proposed method is based on the Domain Adatation network (DAN) using an MMD loss function to reduce the discrepancy between domains. In order to train more effectively, we develop a novel loss function learning strategy in which MMD loss and cross-entropy loss functions are adopted by using different weights according to the progress of each epoch during the learning. The proposed weight adoptation focuses on the training of the source domain in the initial learning phase to learn facial feature information such as eyes, nose, and mouth. After the initial learning is completed, the resulting feature information is used to training a deep network using the target domain images. To evaluate the effectiveness of the proposed method, FR performances were evaluated with pretrained model trained only with CASIA-webface (source images) and fine-tuned model trained only with FERET's gallery (target images) under the same FR scenarios. The experimental results showed that the proposed semi-supervised domain adaptation can be improved by 24.78% compared to the pre-trained model and 28.42% compared to the fine-tuned model. In addition, the proposed method outperformed other state-of-the-arts domain adaptation approaches by 9.41%.

키워드

과제정보

This research was supported by Hankuk University of Foreign Studies Research Fund. This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A2C1092322). This research was supported by the "Development of Wave Overtopping quantitative observation technology" funded by the Korea Institute of Marine Science & Technology Promotion(KIMST) (No. 20220180).

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