Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 14, Issue 5, Pages 1725-1738Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-022-01723-3
Keywords
Occlusion; Face retrieval; Inpainting; Generative adversarial
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This paper proposes an occluded face retrieval method using a generator, discriminator, and deep hashing retrieval network. It reconstructs occluded face images using a face inpainting model and generates compact similarity-preserving hashing codes for better retrieval performance.
COVID-19 has resulted in a significant impact on individual lives, bringing a unique challenge for face retrieval under occlusion. In this paper, an occluded face retrieval method which consists of generator, discriminator, and deep hashing retrieval network is proposed for face retrieval in a large-scale face image dataset under variety of occlusion situations. In the proposed method, occluded face images are firstly reconstructed using a face inpainting model, in which the adversarial loss, reconstruction loss and hash bits loss are combined for training. With the trained model, hash codes of real face images and corresponding reconstructed face images are aimed to be as similar as possible. Then, a deep hashing retrieval network is used to generate compact similarity-preserving hashing codes using reconstructed face images for a better retrieval performance. Experimental results show that the proposed method can successfully generate the reconstructed face images under occlusion. Meanwhile, the proposed deep hashing retrieval network achieves better retrieval performance for occluded face retrieval than existing state-of-the-art deep hashing retrieval methods.
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