Identity verification through the fusion of face and speaker data

Two verification systems, face and speaker, are fused to form a single identity verification system. The Karhunen-Loeve Transform (KLT) is used for dimensional reduction, and a back- propagation neural net is used for classification. Verification involved training a net for each individual in the database for two classes of outputs, `Joe' or `not Joe.' The base speaker identification system used Cepstral analysis for feature extraction and a distortion measure for classification. Verification in this case involved performing the KLT on the Cepstral coefficients and then classifying using a two-class neural net for each individual. KLT feature reduction is compared to alternative linear methods, and the KLT is found to provide superior performance. The fusion of the two base verification systems is shown to provide superior performance over either system alone.