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This paper focuses on multimodal gender recognition. To achieve a robust and discriminative performance for gender recognition, visual observations from both face and corresponding fingerprints are fused to serve for the task. The bag-of-words model is employed to structure the image representation. We propose a novel supervised method to construct the visual words, by which the redundant feature...
We propose to estimate human gender from corresponding fingerprint and face information with the Bayesian hierarchical model. Different from previous works on fingerprint based gender estimation with specially designed features, our method extends to use general local image features. Furthermore, a novel word representation called latent word is designed to work with the Bayesian hierarchical model...
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