Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Independent Component Analysis and Support Vector Machines
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Linear projection methods in face recognition under unconstrained illuminations: a comparative study
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Eigenspace-based face recognition: a comparative study of different approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
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A number of face recognition methods have been proposed. These methods fall into two broad approaches, namely, holistic-based and local-feature-based. Most of holistic-based methods can be classified into two categories, PCA-based and frequency-based categories. This paper introduces a comparison between two holistic-based methods that represent both categories - namely Standard Eigenface method from the PCA-based category and Holistic Fourier Invariant Features (Spectroface) from the frequency-based category. These two methods are tested separately against five main face recognition problems - namely the 3D pose, facial expressions, nonuniform illumination, translation, and scaling - using suitable database(s) for each problem. The results show that the Spectroface method outperforms the Eigenface method in the 3D pose, facial expressions, nonuniform illumination, and translation problems. However, there is no significant difference between both methods in the scaling problem. Finally, in the facial expressions problem, the comparison shows that applying the frequency-based method on the low subband of the wavelet transform is much better than applying the PCA-based method on it.