Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The NIST speaker recognition evaluation - overview methodology, systems, results, perspective
Speech Communication - Speaker recognition and its commercial and forensic applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Face authentication with Gabor information on deformable graphs
IEEE Transactions on Image Processing
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Authenticating corrupted photo images based on noise parameter estimation
Pattern Recognition
Reliability-based decision fusion in multimodal biometric verification systems
EURASIP Journal on Applied 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
Addressing the vulnerabilities of likelihood-ratio-based face verification
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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
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In this paper we extend the recently proposed DCT-mod2 feature extraction technique (which utilizes polynomial coefficients derived from 2D DCT coefficients obtained from horizontally & vertically neighbouring blocks) via the use of various windows and diagonally neighbouring blocks. We also propose enhanced PCA, where traditional PCA feature extraction is combined with DCT-mod2. Results using test images corrupted by a linear and a non-linear illumination change, white Gaussian noise and compression artefacts, show that use of diagonally neighbouring blocks and windowing is detrimental to robustness against illumination changes while being useful for increasing robustness against white noise and compression artefacts. We also show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness against white noise and compression artefacts) while also being robust to illumination changes; moreover, enhanced PCA outperforms PCA with histogram equalisation pre-processing.