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Strategies for improving face recognition from video. Deborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. Flynn Computer Vision Research Lab, University of Notre Dame ( http://www.nd.edu/~cvrl ). Dataset. Goals. Improve performance of face recognition from video
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Strategies for improving face recognition from video Deborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. Flynn Computer Vision Research Lab, University of Notre Dame (http://www.nd.edu/~cvrl) Dataset Goals • Improve performance of face recognition from video • Exploit multiple frames available in a given clip • Select a minimal set of frames to represent the subject • Notre Dame dataset: • 105 subjects – gallery: indoor, probe: outdoor • 3 cameras: HD JVC, DV Canon, iSight Webcam • Honda/UCSD dataset: 20 subjects, 1- 4 clips each Using Mahalanobis cosine distances in PCA space to determine diversity Using K-means clustering to group similar images • Hypothesis: The Mahalanobis cosine distance between images in PCA space reflects their difference • Process: • Project images from subject into PCA space • Pick image with largest total Mahalanobis cosine distance from all others • Pick successive images that are farthest away from the previously selected image • Hypothesis: Image clusters in PCA space represent images that are similar to each other • Process: • Project images from subject into PCA space • Use retained dimensions to cluster images • One image per cluster for N-frame representation Combining quality measure with difference • FaceIt’s faceness measure: Confidence that image contains a face • Three approaches: • LAD: Use Mahalanobis cosine distance to determine distance, use N frames most different from each other • LADHF: Project top 35 frames with highest faceness into PCA space, use N frames most different from each other • CLS: Create K clusters of images and pick image with highest faceness from N clusters for representation • Experiments: • Use up to 20 frames • Compare to choosing images that are equally spaced in time Example images – Notre Dame Dataset Results –Canon as probe and gallery Gallery: Outdoor Probe: Indoor DV Canon HD JVC iSight Webcam Results: UCSD/Honda dataset • Lee et al. • Use clusters in PCA space to determine pose • Posterior probabilities: Identity of current frame conditioned on previous frames • Rank One recognition rate: • 2003: 92.1 % • 2005: 98.9 % • Our approach: • Using 7 frames: 98.8 % Acknowledgements: Biometrics research at the University of Notre Dame is supported bythe National Science Foundation under grant CNS 0130839, by theCentral Intelligence Agency, by the National Geo-Spatial IntelligenceAgency, by UNISYS Corp., and by the US Department of Justice.