Face recognition from a single registered image for conference socializing

    Authors

    Expert Syst. Appl.

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Child Abuse Negl.

    Keywords

    Conference socializing; Face recognition; Single registered image; Large; pose variation; ONE TRAINING IMAGE; 2-DIMENSIONAL PCA; REPRESENTATION; EIGENFACES; SAMPLE; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic; Operations Research & Management Science

    Abstract

    Scientific conferences are primary venues for connecting with and forming relationships with fellow researchers and scientists. Thus, over the course of a conference participants often take advantage of the many opportunities to network. In this setting, it is desirable to quickly recognize the identity of the persons we see and wish to meet. In particular, it could be embarrassing to not recognize a prominent researcher. In this paper, we investigate a novel face recognition framework that is applicable to conference socialization scenarios. In the proposed framework, only frontal images are used as training images; and face recognition is possible from an arbitrary view of a subject. Our system prototype assumes that the conference participants have uploaded a frontal photo during the registration process. At the conference, the identity of a person can be recognized from a picture, taken from an arbitrary angle with a standard mobile phone. Our experimental results indicate that the proposed framework is robust to possible large pose variations between the non-frontal image captured impromptu and the training image of the same person. Experiments based upon standard face dataset and real conference socializing datasets are conducted to test the effectiveness of the proposed techniques. (C) 2014 Elsevier Ltd. All rights reserved.

    Subjects

    Y. Zhao; Y. Liu; Y. Liu; S. H. Zhong;K. A. Hua

    Volume

    42

    Issue/Number

    3

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    973

    Last Page

    979

    WOS Identifier

    WOS:000345734700001

    ISSN

    0957-4174

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