Abstract
Owing to the development of recognition sensor technologies, the Smart Home, which uses context awareness sensors, has recently garnered much attention. Strategies to identify a specific user are being actively pursued, as this is a necessary component in realizing the Smart Home concept: systems must analyze various users’ motions and recognize their input in a multiuser environment. Facial recognition is one method for specifying various users a Smart Home environment. Through facial recognition, the system prioritizes users who intentionally stare the system’s camera. In this study, we implemented a user selection system by combining two algorithms: AdaBoost and Mean Shift. The AdaBoost algorithm identifies Harr-like features and enhances the overall algorithm’s accuracy by resolving the interference or local minima problems in Mean Shift. The Mean Shift algorithm tracks a region of interest at high speed and compensates for AdaBoost’s disadvantages by reducing false positives. We envision that the proposed algorithm will has potential applications in a Smart Home setting.
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