strategies for improving face recognition from video

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) Goals Dataset • 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 • 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 Using Mahalanobis cosine distances in PCA space to determine diversity Results: UCSD/Honda dataset Combining quality measure with difference Acknowledgements: Biometrics research at the University of Notre Dame is supported by the National Science Foundation under grant CNS 0130839, by the Central Intelligence Agency, by the National Geo-Spatial Intelligence Agency, by UNISYS Corp., and by the US Department of Justice. Using K-means clustering to group similar images • 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 •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 • 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 Results –Canon as probe and gallery • 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 % Example images – Notre Dame Dataset Gallery : Outdoor Probe: Indoor DV Canon HD JVC iSight Webcam R ank O ne R ecognition R ate 0 20 40 60 80 100 120 1 3 5 7 9 11 13 15 1719 21 23 25 27 29 31 33 35 Num ber offram es R ank O ne R ecognition R ate Kriegman,2003 Kriegman,2005 NEHF R ank O ne R ecognition R ate 0 10 20 30 40 50 60 70 80 90 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Num ber offram es R ank one R ecognition R ate LA D LA D H F C LS EST

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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 - PowerPoint PPT Presentation

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Page 1: Strategies for improving face recognition from video

Strategies for improving face recognition from videoDeborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. Flynn

Computer Vision Research Lab, University of Notre Dame (http://www.nd.edu/~cvrl)

Goals Dataset

• 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

• 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

Using Mahalanobis cosine distances in PCA space to determine diversity

Results: UCSD/Honda dataset

Combining quality measure with difference

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.

Using K-means clustering to group similar images

• 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

• 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

• 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

Results –Canon as probe and gallery

• 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 %

Example images – Notre Dame Dataset

Gal

lery

: O

utd

rob

Ind

DV Canon HD JVC iSight Webcam

Rank One Recognition Rate

100