Comparison of weighting strategies in early and late fusion approaches to audio-visual person authentication

Person authentication can be strongly enhanced by the combination of different modalities. This is also true for the face and voice signals, which can be obtained with minimal inconvenience for the user. However, features from each modality can be combined at various different levels of processing and for face and voice signals the advantage of fusion depends strongly on the way they are combined. The aim of the work presented is to investigate the optimal strategy for combining voice and face modalities for signals of varying quality. The experimental data are taken from a newly acquired database using a PDA, which contains audio-visual recordings in different conditions. Voice features use mel-frequency cepstral coefficients, while the face signal is parameterised using wavelet coefficients in certain subbands. Results are presented for both early (feature-level) and late (score-level) fusion. At each level different fixed and variable weightings are used, both to weight between frames within each modality and to weight between modalities, where weights are based on some measure of signal reliability, such as the accuracy of automatic face detection or the audio signal to noise ratio. In addition, the contribution to authentication of information from different areas of the face is explored to determine a regional weighting for the face coefficients.