Abstract
It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm first performs a holistic search using the whole image features and selects probable candidates for further processing. Then the facial features of these probable candidates are compared and classified with the stored patterns in the train set. Finally the classification results are fused with a weighted majority voting to form the final decision. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method.
Original language | English |
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Title of host publication | 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISBN (Print) | 1424407796, 9781424407798 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Externally published | Yes |
Event | IEEE International Symposium on Signal Processing and its Applications 2007 - Sharjah, United Arab Emirates Duration: 12 Feb 2007 → 15 Feb 2007 Conference number: 9th https://ieeexplore.ieee.org/xpl/conhome/4542559/proceeding (Proceedings) |
Publication series
Name | 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings |
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Conference
Conference | IEEE International Symposium on Signal Processing and its Applications 2007 |
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Abbreviated title | ISSPA 2007 |
Country/Territory | United Arab Emirates |
City | Sharjah |
Period | 12/02/07 → 15/02/07 |
Internet address |