A Quantified Study of Facial Asymmetry in 3D Faces
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Local facial asymmetry for expression classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Facial asymmetry in frequency domain: the "phase" connection
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A one bit facial asymmetry code (FAC) in fourier domain for human recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Facial asymmetry: a new robust biometric in the frequency domain
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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Even though numerous techniques for face recognition have been explored over the years, most research has primarily focussed on identification from full frontal/profile facial images. This paper conducts a first systemic study to assess the performance when using partial-faces for identification. Our specific approach considers an ensemble of Radial Basis Function (RBF) Networks. A specific advantage of using an ensemble is its ability to cope with the inherent variability in the image formation and data acquisition process. Our database consists of imagery corresponding to 150 unique subjects totaling to 3,000 facial images with +/- 5 degree rotation. Based on our experimental results, we observe that the average Cross Validation performance is the same even if only half the face image is used instead of the full-face image. Specifically we obtain 96% when partial-faces are used and 97% when full-faces are used.