TY - GEN
T1 - Localization of skin features on the hand and wrist from small image patches
AU - Stearns, Lee
AU - Oh, Uran
AU - Cheng, Bridget J.
AU - Findlater, Leah
AU - Ross, David
AU - Chellappa, Rama
AU - Froehlich, Jon E.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Skin-based biometrics rely on the distinctiveness of skin patterns across individuals for identification. In this paper, we investigate whether small image patches of the skin can be localized on a user's body, determining not 'who?' instead 'where?' Applying techniques from biometrics and computer vision, we introduce a hierarchical classifier that estimates a location from the image texture and refines the estimate with keypoint matching and geometric verification. To evaluate our approach, we collected 10,198 close-up images of 17 hand and wrist locations across 30 participants. Within-person algorithmic experiments demonstrate that an individual's own skin features can be used to localize their skin surface image patches with an F1 score of 96.5%. As secondary analyses, we assess the effects of training set size and between-person classification. We close with a discussion of the strengths and limitations of our approach and evaluation methods as well as implications for future applications using a wearable camera to support touch-based, location-specific taps and gestures on the surface of the skin.
AB - Skin-based biometrics rely on the distinctiveness of skin patterns across individuals for identification. In this paper, we investigate whether small image patches of the skin can be localized on a user's body, determining not 'who?' instead 'where?' Applying techniques from biometrics and computer vision, we introduce a hierarchical classifier that estimates a location from the image texture and refines the estimate with keypoint matching and geometric verification. To evaluate our approach, we collected 10,198 close-up images of 17 hand and wrist locations across 30 participants. Within-person algorithmic experiments demonstrate that an individual's own skin features can be used to localize their skin surface image patches with an F1 score of 96.5%. As secondary analyses, we assess the effects of training set size and between-person classification. We close with a discussion of the strengths and limitations of our approach and evaluation methods as well as implications for future applications using a wearable camera to support touch-based, location-specific taps and gestures on the surface of the skin.
KW - Biometrics and computer vision applications
KW - On-body input
KW - Skin texture classification
UR - http://www.scopus.com/inward/record.url?scp=85019101937&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2016.7899767
DO - 10.1109/ICPR.2016.7899767
M3 - Conference contribution
AN - SCOPUS:85019101937
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1003
EP - 1010
BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Pattern Recognition, ICPR 2016
Y2 - 4 December 2016 through 8 December 2016
ER -