On probabilistic combination of face and gait cues for identification

We approach the task of person identification based on face and gait cues. The cues are derived from multiple simultaneous camera views, combined through the visual hull algorithm to create imagery in canonical pose prior to recognition. These view-normalized sequences, containing frontal images of face and profile silhouettes, are separately used for face and gait recognition, and the results may be combined using a range of strategies. We discuss the issues of cross-modal correlation and score transformations for different modalities, present the probabilistic settings for the cross-modal fusion and explore several common fusion approaches. The effectiveness of various strategies is evaluated on a data set with 26 subjects. We hope that the discussion presented in this paper may be useful in developing further statistical frameworks for multi-modal recognition.