Machine Learning
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Soft biometric traits for continuous user authentication
IEEE Transactions on Information Forensics and Security
Integration of a low-cost RGB-D sensor in a social robot for gesture recognition
Proceedings of the 6th international conference on Human-robot interaction
Depth information in human gait analysis: an experimental study on gender recognition
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
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An intuitive and robust user recognition system is at the key of a natural interaction between a social robot and its users. The gender of a new user can be guessed without explicitly asking it of her, which can then be used to personalize the interaction flow. In this LBR, a novel algorithm is used to estimate the gender of a person based on its morphological shape. More specifically, the vertical outline of the breast of the user is used to estimate his or her gender, based on similar shapes seen during training. On early benchmarks with databases that represent well the diversity of human body shapes, the accuracy rate is close to 90% and outperforms a state-of-the-art algorithm. Our algorithm provides a fast and seamless estimation flow and needs limited computation resources, which tailor it for HRI. Its usefulness has been proved by integrating it in a social robot. However, its use raises concerns among the users about their privacy, which will lead to further study.