A biometric database with rotating head videos and hand- drawn face sketches
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Hand-drawn face sketch recognition by humans and a PCA-based algorithm for forensic applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
A bi-objective optimization model for interactive face retrieval
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Face image retrieval across age variation using relevance feedback
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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This paper reports a human face image searching system using sketches. A two-phase method, namely, sketch-to-mug-shot matching and human face image searching using relevance feedback, is designed and developed. In the sketch-to-mug-shot matching phase, we have developed a facial feature matching algorithm using local and global features. A point distribution model is employed to represent local facial features while the global feature consists of a set of the geometrical relationship between facial features. It is found that the performance of the sketch-to-mug-shot matching is good if the sketch image looks like the mug shot image in the database. However, in some situations, it is hard to construct a sketch that looks like the photograph. To overcome this limitation, this paper makes use of the concept of ldquohuman-in-the-looprdquo and proposes a human face image searching algorithm using relevance feedback in the second phase. Positive and negative samples will be collected from the user. A feedback algorithm that employs subspace linear discriminant analysis for online learning of the optimal projection for face representation is then designed and developed. The proposed system has been evaluated using the FERET database and a Japanese database with hundreds of individuals. The results are encouraging.