Abstract
This paper presents a novel approach for video-based face recognition. We define a metric based on an average L 2 Euclidean distance between two videos as the classifier. This metric makes use of Earth Mover’s Distance (EMD) as the underlying similarity measurement between videos. Earth Mover’s Distance is a recently proposed metric for geometric pattern matching and it reflects the average ground distance between two distributions. Under the framework of EMD, each video is modeled as a video signature and Euclidean distance is selected as the ground distance of EMD. Since clustering algorithm is employed, video signature can well represent the overall data distribution of faces in video. Experimental results demonstrate the superior performance of our algorithm.
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References
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey., Technical Reports of Computer Vision Laboratory of University of Maryland (2000)
Zhou, S., Chellappa, R.: Probabilistic Human Recognition from Video. In: Proceedings of the European Conference On Computer Vision (2002)
Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-Based Face Recognition Using Probabilistic Appearance Manifolds. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2003)
Liu, X., Chen, T.: Video-Based Face Recognition Using Adaptive Hidden Markov Models. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (2003)
Yamaguchi, O., Fukui, K., Maeda, K.: Face Recognition using Temporal Image Sequence. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (1998)
Shakhnarovich, G., Fisher, J.W., Darrell, T.: Face recognition from long-term observations. In: Proceedings of the European Conference On Computer Vision (2002)
Dantzig, G.B.: Application of the simplex method to a transportation problem. In: Activity Analysis of Production and Allocation, pp. 359–373. John Wiley and Sons, Chichester (1951)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
Rubner, Y., Tomasi, C., Guibas, L.J.: Adaptive Color-Image Embedding for Database Navigation. In: Proceedings of the Asian Conference on Computer Vision (1998)
“Learning Vector Quantization (LVQ)”, http://www.willamette.edu/~gorr/classes/cs449/Unsupervised/competitive.html
Keselman, Y., Shokoufandeh, A., Demirci, M.F., Dickinson, S.: Many-to-Many Graph Matching via Metric Embedding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2003)
Demirci, M.F., Shokoufandeh, A., Keselman, Y., Dickinson, S., Bretzner, L.: Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs. In: Proceedings of the 8th European Conference on Computer Vision (2004)
Stolfi, J.: Personal Communication (1994)
Cohen, S., Guibas, L.: The Earth Mover’s Distance under Transformation Sets. In: Proceedings of the 7th IEEE International Conference On Computer Vision (1999)
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© 2004 Springer-Verlag Berlin Heidelberg
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Li, J., Wang, Y., Tan, T. (2004). Video-Based Face Recognition Using a Metric of Average Euclidean Distance. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_26
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DOI: https://doi.org/10.1007/978-3-540-30548-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24029-7
Online ISBN: 978-3-540-30548-4
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