期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 9, 期 12, 页码 2132-2143出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2014.2359548
关键词
Face recognition; graph analysis; centrality measure; alignment-free; pose robust
资金
- National Science Foundation [0905671, 0915270, 1330110]
- Direct For Computer & Info Scie & Enginr [0905671] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1330110] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems [0905671] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0915270] Funding Source: National Science Foundation
Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFC). An REG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFC. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFC-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The REG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation.
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