Digital Image Processing
On Probabilistic Combination of Face and Gait Cues for Identification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Integrating Face and Gait for Human Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Feature Fusion of Face and Gait for Human Recognition at a Distance in Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Integrating Face and Gait for Human Recognition at a Distance in Video
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Frontal-view gait recognition by intra- and inter-frame rectangle size distribution
Pattern Recognition Letters
Factorial HMM and parallel HMM for gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gait flow image: A silhouette-based gait representation for human identification
Pattern Recognition
Feature relationships hypergraph for multimodal recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Multiple ROI selection based focal liver lesion classification in ultrasound images
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
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Video-based human recognition at a distance remains a challenging problem for the fusion of multimodal biometrics. As compared to the approach based on match score level fusion, in this paper, we present a new approach that utilizes and integrates information from side face and gait at the feature level. The features of face and gait are obtained separately using principal component analysis (PCA) from enhanced side face image (ESFI) and gait energy image (GEI), respectively. Multiple discriminant analysis (MDA) is employed on the concatenated features of face and gait to obtain discriminating synthetic features. This process allows the generation of better features and reduces the curse of dimensionality. The proposed scheme is tested using two comparative data sets to show the effect of changing clothes and face changing over time. Moreover, the proposed feature level fusion is compared with the match score level fusion and another feature level fusion scheme. The experimental results demonstrate that the synthetic features, encoding both side face and gait information, carry more discriminating power than the individual biometrics features, and the proposed feature level fusion scheme outperforms the match score level and another feature level fusion scheme. The performance of different fusion schemes is also shown as cumulative match characteristic (CMC) curves. They further demonstrate the strength of the proposed fusion scheme.