Face recognition with local steerable phase feature
Pattern Recognition Letters
Face recognition with local steerable phase feature
Pattern Recognition Letters
Video parsing based on head tracking and face recognition
Proceedings of the 6th ACM international conference on Image and video retrieval
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Secure human face authentication for mobile e-government transactions
International Journal of Mobile Communications
Determining discriminative anatomical point pairings using adaboost for 3D face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fusing local patterns of gabor magnitude and phase for face recognition
IEEE Transactions on Image Processing
Second-Order (non-fourier) attention-based face detection
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A binary decision tree implementation of a boosted strong classifier
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Multistage face recognition using adaptive feature selection and classification
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Boosting statistical local feature based classifiers for face recognition
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Boosting local binary pattern (LBP)-Based face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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In this paper, we present a method for face recognition using boosted Gabor feature based classifiers. Weak classifiers are constructed based on both magnitude and phase features derived from Gabor filters [Quadrature-phase simple-cell pairs are ap-propriately described in complex analytic from]. The multi-class problem is transformed into a two-class one ofintra- and extra-class classification using intra-personal and extra-personal difference images, as in [Beyond euclidean eigenspaces:bayesian matching for visian recognition]. A cascade of strong classifiers are learned using bootstrapped negative examples, similar to the way in face detection framework [Robust real time object detection]. The combination of classifiers based on two different types of features produces better results than using either type. Experiments on FERET database show good results comparable to the best one reported in literature [The FERET evaluation methodology for face-recognition algorithms].