The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Eye localization for face matching: is it always useful and under what conditions?
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Eye localization in low and standard definition content with application to face matching
Computer Vision and Image Understanding
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Extraction of normalized face from input images is an essential preprocessing step of many face recognition algorithms. Typical face extraction algorithms make use of the locations of facial features, such as the center of eyes, that are marked either manually or automatically. It is not guaranteed, however, that we always obtain the exact or optimal locations of the eye centers, and using inaccurate landmark locations, and consequently poorly registered faces, is one of the main causes of performance degradation in appearance-based face recognition. Moreover, in some applications, it is hard to verify the correctness of the face extraction for every query image. For improved performance and robustness to the eye location variation, we propose an eye perturbation approach that generates multiple face extractions from a query image by using the perturbed eye locations centered at the initial eye locations. The extracted faces are then matched against the enrolled gallery set to produce individual similarity scores. Final decisions can be made by using various committee methods – nearest neighbor, maximum vote, etc. – of combining the results of individual classifiers. We conclude that the proposed eye perturbation approach with nearest neighbor classification improves recognition performance and makes existing face recognition algorithms robust to eye localization errors.