An efficient reliability estimation technique for audio-visual person identification

This paper presents a reliability estimation technique for an Audio-Visual (AV) person identification system. The estimated reliability measures are mapped to modality weights by using a mapping function. The weighted sum rule of fusion was used to fuse the audio and visual modalities before reaching a decision about the identity of a person. The proposed technique was tested on 88 subjects from the AusTalk AV database. Experimental results show that the proposed technique improves the identification accuracy of the min-max normalization, compared to when it is used without any reliability estimation. The identification accuracy of the proposed technique is also higher than the non-learned approach of modality weight adaptation.