A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Authenticating corrupted photo images based on noise parameter estimation
Pattern Recognition
Face recognition under arbitrary illumination using illuminated exemplars
Pattern Recognition
Face detection and facial component extraction by wavelet decomposition and support vector machines
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A new algorithm for age recognition from facial images
Signal Processing
Authenticating corrupted face image based on noise model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Authenticating corrupted facial images on stand-alone DSP system
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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Human face is one of the most common and useful keys to a person's identity. Many algorithms have been developed for automatic face recognition. And a number of commercial products have reached the market already. In general, however, many believe that the technology has yet to improve further, particularly to overcome the instability due to variable illuminations, expressions, poses and accessories. These variations often lead to large nonlinear variation in facial image. To date it is a very important issue to understand the limitation of the current face recognition technology. In this paper, we report the experimental result of face recognition performed using PCA (Principal Component Analysis), LFA(Local Feature Analysis) and correlation matching algorithms on the KFDB(Korean Face Database) which contains Korean face images taken under various conditions.