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Appl."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>To overcome the technical bottleneck of face recognition in low-light scenarios, Near-InfraRed and VISible (NIR-VIS) heterogeneous face recognition is proposed for matching well-lit VIS faces with poorly lit NIR faces. Current cross-modal synthesis methods visually convert the NIR modality to the VIS modality and then perform face matching in the VIS modality. However, using a heavyweight GAN network on unpaired NIR-VIS faces may lead to high synthesis difficulty, low inference efficiency, and other problems. To alleviate the above problems, we simultaneously synthesize NIR and VIS images into modality-independent syncretic images and propose a novel syncretic space learning (SSL) model to eliminate the modal gap. First, Syncretic Modality Generator (SMG) synthesizes NIR and VIS images into syncretic images using channel-level convolution with a shallow CNN. In particular, the discriminative structural information is well preserved and the face quality can be further improved with small modal variations in a self-supervised learning manner. Second, Modality-adversarial Syncretic space Learning (MSL) projects NIR and VIS images into the syncretic space by a syncretic-modality adversarial learning strategy with syncretic pattern guided objective, so the modal gap of NIR-VIS faces can be effectively reduced. Finally, the Syncretic Distribution Consistency (SDC) constructed by NIR-syncretic, syncretic-syncretic, and VIS-syncretic consistency can enhance the intra-class compactness and learn discriminative representations. Extensive experiments on three challenging datasets demonstrate the effectiveness of the SSL method.<\/jats:p>","DOI":"10.1145\/3607143","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T13:20:16Z","timestamp":1688476816000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Syncretic Space Learning Network for NIR-VIS Face Recognition"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7466-1041","authenticated-orcid":false,"given":"Yiming","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Technology, Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2886-7346","authenticated-orcid":false,"given":"Weipeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4884-323X","authenticated-orcid":false,"given":"Haifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Technology, Sun Yat-sen University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"242","volume-title":"ISICA","author":"Chen Jie","year":"2020","unstructured":"Jie Chen, Gang Liu, and Xin Chen. 2020. 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