Presence: Teleoperators and Virtual Environments
Human Motion Analysis Using Eroded and Restored Skeletons
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Using ground reaction forces from gait analysis: body mass as a weak biometric
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Identifying user traits by mining smart phone accelerometer data
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Using height sensors for biometric identification in multi-resident homes
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
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We present a parametric method to automatically identify people in monocular low-resolution video by estimating the height and stride parameters of their gait. Stride parameters (stride length and cadence) are functions of body height, weight, and gender. Previous work has demonstrated effective use of these biometrics for identification and verification of people. In this paper, we show that performance is significantly improved by using height as an additional discriminant feature. Height is estimated by segmenting the person from the background and fitting their apparent height to a time-dependent model. With a database of 45 people and 4samples of each, we show that a person is correctly identified with 49% probability when using both height and stride parameters, compared with 21% when using stride parameters only. Height estimates for this configuration are accurate to within s = 3.5cm. This method works with low-resolution images of people, and is robust to changes in lighting, clothing, and tracking errors.