CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cancelable Biometric Filters for Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Face Recognition with Renewable and Privacy Preserving Binary Templates
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
On solving the face recognition problem with one training sample per subject
Pattern Recognition
Journal of Cognitive Neuroscience
Biometric hash: high-confidence face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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Producing changeable biometric templates from limited number of human biometric traits is important for the deployment of biometric technology in a wide variety of applications. This paper introduces a new method for changeable face verification using random translational transformation. The proposed method is based on translating the original feature vectors by adding a randomly generated vector. The sorted index numbers of the resulting vector is stored as the template for verification. The random translational transformation in conjunction with the sorted index number approach constitutes a non-invertible transformation, and hence the privacy of the users can be protected. It is shown that the proposed method is computationally simple, and is capable of generating templates with strong changeability. The effectiveness of the proposed method is well supported by both the detailed analysis and extensive experimentation.