TY - JOUR
T1 - Face recognition from a single registered image for conference socializing
AU - Zhao, Yu
AU - Liu, Yan
AU - LIU, Yang
AU - Zhong, Shenghua
AU - Hua, Kien A.
N1 - Funding Information:
This work was supported by grant G-UA69: Implicit Learning for Image/Video Retrieval on Large Scale Datasets.
PY - 2015/2/15
Y1 - 2015/2/15
N2 - 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.
AB - 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.
KW - Conference socializing
KW - Face recognition
KW - Large pose variation
KW - Single registered image
UR - http://www.scopus.com/inward/record.url?scp=84908102201&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2014.08.016
DO - 10.1016/j.eswa.2014.08.016
M3 - Journal article
AN - SCOPUS:84908102201
SN - 0957-4174
VL - 42
SP - 973
EP - 979
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 3
ER -