Edge detection and motion detection
Image and Vision Computing
Automatic extraction of face-features
Pattern Recognition Letters
Human face profile recognition by computer
Pattern Recognition
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysing front view face profiles for face recognition via the Walsh transform
Pattern Recognition Letters
Human face recognition and the face image set's topology
CVGIP: Image Understanding
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global and Local Active Contours for Head Boundary Extraction
International Journal of Computer Vision
Face Detection From Color Images Using a Fuzzy Pattern Matching Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Local Fisher Features
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Face recognition in non-uniform illumination conditions using lighting normalization and SVM
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Multiple face contour detection based on geometric active contours
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Multiple face contour detection using adaptive flows
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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Many features can be used to describe a human face but few have been used in combination. Extending the feature vector using orthogonal sets of measurements can reduce the variance of a matching measure, to improve discrimination capability. This paper investigates how different features can be used for discrimination, alone or when integrated into an extended feature vector.This study concentrates on improving feature definition and extraction from a frontal view image, incorporating and extending established measurements. These form an extended feature vector based on four feature sets: geometric (distance) measurements, the eye region, the outline contour, and the profile. The profile, contour, and eye region are described by the Walsh power spectrum, normalized Fourier descriptors, and normalized moments, respectively. Although there is some correlation between the geometrical measures and the other sets, their bases (distance, shape description, sequency, and statistics) are orthogonal and hence appropriate for this research.A database of face images was analyzed using two matching measures which were developed to control differently the contributions of elements of the feature sets. The match was evaluated for both measures for the separate feature sets and for the extended feature vector. Results demonstrated that no feature set alone was sufficient for recognition whereas the extended feature vector could discriminate between subjects successfully.