An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiences with a mobile robotic guide for the elderly
Eighteenth national conference on Artificial intelligence
An Incremental Learning Method for Face Recognition under Continuous Video Stream
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Interactive humanoid robots for a science museum
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Content based indexing of images and video using face detection and recognition methods
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Interactive robots as social partners and peer tutors for children: a field trial
Human-Computer Interaction
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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We present an incremental and unsupervised face recognition system and evaluate it offline using data which were automatically collected by Mertz, a robotic platform embedded in real human environment. In an eight-day-long experiment, the robot autonomously detects, tracks, and segments face images during spontaneous interactions with over 500 passersby in public spaces and automatically generates a data set of over 100,000 face images. We describe and evaluate a novel face clustering algorithm using these data (without any manual processing) and also on an existing face recognition database. The face clustering algorithm yields good and robust performance despite the extremely noisy data segmented from the realistic and difficult public environment. In an incremental recognition scheme evaluation, the system is correct 74% of the time when it declares "I don't know this person" and 75.1% of the time when it declares" I know this person, he/she is ..." The latter accuracy improves to 83.8% if the system is allowed some learning curve delay in the beginning.