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
Retinal vision applied to facial features detection and face authentication
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Handbook of Face Recognition
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Personal identification using periocular skin texture
Proceedings of the 2010 ACM Symposium on Applied Computing
On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Quality-based conditional processing in multi-biometrics: application to sensor interoperability
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Periocular Biometrics in the Visible Spectrum
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Circuits and Systems for Video Technology
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We present a new system for biometric recognition using periocular images based on retinotopic sampling grids and Gabor analysis of the local power spectrum. A number of aspects are studied, including: 1) grid adaptation to dimensions of the target eye vs. grids of constant size, 2) comparison between circular- and rectangular-shaped grids, 3) use of Gabor magnitude vs. phase vectors for recognition, 4) rotation compensation between query and test images, and 5) comparison with an iris machine expert. Results show that our system achieves competitive verification rates compared with other periocular recognition approaches. We also show that top verification rates can be obtained without rotation compensation, thus allowing to remove this step for computational efficiency. Also, the performance is not affected substantially if we use a grid of fixed dimensions, or it is even better in certain situations, avoiding the need of accurate detection of the iris region.