Multimodality gender estimation using Bayesian hierarchical model

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. The feature representation is embedded to our multimodality model, within which the information from fingerprint and face is fused at the decision level for gender estimation. Experiments on our internal database show the promising performance.