IEEE: Face Recognition and Tracking for Human-Robot Interaction

[ashwin kumar]

Abstract - This paperpresents a design and experimental
study of human-robot interaction via face recognition and
image tracking. A new architecture is proposed for fast
face recognition of family members. In the proposed
system, each family member has hidher own RBF neural
networks. Each neural network is only responsible for
recognizing its trained member. Consequently, rhe
database is small and the processing time required for
face recognition is minimized. A recognition rate of 94%
has been achieved, an improvement relative to
conventional approaches. In order to detect and track a
person, we also developed an algorithm for detecting
multiple faces in a scene based on division of skin and
hair color regions. The face recognifion and imoge
tracking system has been integrated to an experiniental
mobile robot. Practical experiments reveal that the robot
demonstrates real-time face recognition and tracking
performance.

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Ashwin,
9742738409