{"type":"ScholarlyArticle","title":"Live Face Verification with Multiple Instantialized Local Homographic Parameterization","authors":[{"name":"Chen Lin","slug":"chen-lin","given":"Chen","family":"Lin","affiliation":"Shanghai Jiao Tong University"},{"name":"Zhouyingcheng Liao","slug":"zhouyingcheng-liao","given":"Zhouyingcheng","family":"Liao","affiliation":"Shanghai Jiao Tong University"},{"name":"Peng Zhou","slug":"peng-zhou","given":"Peng","family":"Zhou","affiliation":"Shanghai Jiao Tong University"},{"name":"Jianguo Hu","slug":"jianguo-hu","given":"Jianguo","family":"Hu","affiliation":"Shanghai Institute for Advanced Communication and Data Sci- ence , Shanghai Key Laboratory of Digital Media Processing and Transmission , Shanghai Jiao Tong University , Shanghai 200240 , China."},{"name":"Bingbing Ni","slug":"bingbing-ni","given":"Bingbing","family":"Ni","affiliation":"Shanghai Jiao Tong University"}],"abstract":"State-of-the-art live face verification methods would easily be attacked by recorded facial expression sequence. This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure. This method directly explores the underlying subtle motion difference between the facial movements re-captured from a planer screen (e.g., a pad) and those from a real face; therefore interactive facial expression is no longer required. Furthermore, inspired by the fact that ?a fake facial movement sequence MUST contains many patch-wise fake sequences?, we embed our network into a multiple instance learning framework, which further enhance the recall rate of the proposed technique. Extensive experimental results on several face benchmarks well demonstrate the superior performance of our method.","doi":"10.24963/ijcai.2018/113","url":"https://ijcai.org/proceedings/2018/113","pdf":"https://ijcai.org/proceedings/2018/0113.pdf","markdown":"https://ijcai.org/proceedings/2018/113.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/113","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":113,"pages":{"first":814,"last":820},"keywords":["Computer Vision: Biometrics, Face and Gesture Recognition","Machine Learning Applications: Other Applications"],"videos":[],"references":[{"position":1,"raw":"[Andrews and Hofmann, 2003] Stuart Andrews and Thomas Hofmann. 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