The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Journal of Cognitive Neuroscience
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Face authentication using adapted local binary pattern histograms
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Highly accurate and fast face recognition using near infrared images
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Boosting local binary pattern (LBP)-Based face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Several studies of psychophysics have shown that the eyes or the mouth seem to be an important cue in human face perception, and the nose plays an insignificant role, this means that there exists a distinctive information distribution of faces.This paper presents a novel approach for face recognition by combining the Local Binary Patterns (LBP) based face descriptor and the distinctive information of faces First, we give a quantitative estimation of the density for each pixel in fronted face image by combining Parzen-window approach and Scale Invariant Feature Transform (SIFT) detector, which is taken as the measure of the distinctive information of the faces Second, we integral the density function in the sub-window region of face to gain the weights set which is used in the LBP based face descriptor to produce weighted Chi square statistics As an elementary application of the estimation of distinctive information of face, the proposed method is tested on the FERET FA/FB image sets and yields a recognition rate of 98.2% contrast to the 97.3% which is produced by the method adopted by Ahonen.