Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Modal 2D and 3D Biometrics for Face Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition using multiple facial features
Pattern Recognition Letters
Outdoor recognition at a distance by fusing gait and face
Image and Vision Computing
A probabilistic fusion methodology for face recognition
EURASIP Journal on Applied Signal Processing
Feature fusion of side face and gait for video-based human identification
Pattern Recognition
Fusing gait and face cues for human gender recognition
Neurocomputing
Gender Recognition Based on Fusion of Face and Multi-view Gait
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Human recognition on combining kinematic and stationary features
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Text-independent speaker identification using VQ-HMM model based multiple classifier system
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Multi-biometrics using facial appearance, shape and temperature
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Human recognition at a distance in video by integrating face profile and gait
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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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 framework for multi-modal recognition.