Sensors 2013, 13(6), 7884-7901; doi:10.3390/s130607884

Gait-Based Person Identification Robust to Changes in Appearance

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Received: 28 April 2013; in revised form: 10 June 2013 / Accepted: 14 June 2013 / Published: 19 June 2013
(This article belongs to the Section Physical Sensors)
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Abstract: The identification of a person from gait images is generally sensitive to appearance changes, such as variations of clothes and belongings. One possibility to deal with this problem is to collect possible subjects’ appearance changes in a database. However, it is almost impossible to predict all appearance changes in advance. In this paper, we propose a novel method, which allows robustly identifying people in spite of changes in appearance, without using a database of predicted appearance changes. In the proposed method, firstly, the human body image is divided into multiple areas, and features for each area are extracted. Next, a matching weight for each area is estimated based on the similarity between the extracted features and those in the database for standard clothes. Finally, the subject is identified by weighted integration of similarities in all areas. Experiments using the gait database CASIA show the best correct classification rate compared with conventional methods experiments.
Keywords: gait; person identification; affine moment invariants; local features
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Iwashita, Y.; Uchino, K.; Kurazume, R. Gait-Based Person Identification Robust to Changes in Appearance. Sensors 2013, 13, 7884-7901.

AMA Style

Iwashita Y, Uchino K, Kurazume R. Gait-Based Person Identification Robust to Changes in Appearance. Sensors. 2013; 13(6):7884-7901.

Chicago/Turabian Style

Iwashita, Yumi; Uchino, Koji; Kurazume, Ryo. 2013. "Gait-Based Person Identification Robust to Changes in Appearance." Sensors 13, no. 6: 7884-7901.

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