Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89500
Title: A further study of low resolution androgenic hair patterns as a soft biometric trait
Authors: Chan, Frodo Kin Sun
Kong, Adams Wai Kin
Keywords: Soft Biometrics
Biometrics
Issue Date: 2018
Source: Chan, F. K. S., & Kong, A. W. K. (2018). A further study of low resolution androgenic hair patterns as a soft biometric trait. Image and Vision Computing, 69,125-142.
Series/Report no.: Image and Vision Computing
Abstract: Soft biometric traits such as skin color, tattoos, shoe size, height, and weight have been regularly used for forensic investigation, especially when hard biometric traits, e.g., faces and fingerprints are not available. Recently, a new soft biometric trait, androgenic hair also called body hair, was evaluated. The previous study showed that low resolution androgenic hair patterns have potential for forensic investigation. However, it was believed that they are not a distinctive biometric trait because of the reported accuracy. To explore discriminative information in androgenic hair patterns, in this paper, a new algorithm, which makes use of leg geometry to align lower leg images, large feature sets (about 60,000 features) extracted through multi-directional grid systems to increase discriminative power and robustness, and class-specific partial least squares (PLS) models to utilize the features effectively, is employed. To further enhance the performance of the class-specific PLS models trained on very limited positive samples, one to three images per model in the experiments, and further enhance robustness against viewpoint and pose variations, a scheme is designed to generate more positive samples from a single image. Experimental results on 1493 low resolution leg images with large viewpoint and pose variations from 412 legs demonstrate that low resolution androgenic hair patterns contain rich information and the impression of low discriminative power on androgenic hair is due to the method used in the previous study.
URI: https://hdl.handle.net/10356/89500
http://hdl.handle.net/10220/44918
ISSN: 0262-8856
DOI: http://dx.doi.org/10.1016/j.imavis.2017.08.009
Rights: © 2017 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Image and Vision Computing, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.imavis.2017.08.009].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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