Artificial Intelligence Review - Special issue on lazy learning
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
JGram: Rapid Development of Multi-Agent Pipelines for Real-World Tasks
ASAMA '99 Proceedings of the First International Symposium on Agent Systems and Applications Third International Symposium on Mobile Agents
Memory-Based Face Recognition for Visitor Identification
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Efficient region-based motion segmentation for a video monitoring system
Pattern Recognition Letters
An Incremental Learning Algorithm for Face Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
A Real-Time Region-Based Motion Segmentation Using Adaptive Thresholding and K-Means Clustering
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Real-time implementation of face recognition algorithms on DSP chip
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Incremental template updating for face recognition in home environments
Pattern Recognition
Semi-supervised PCA-Based face recognition using self-training
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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When you've visited someone in a large apartment or office complex, chances are that a security guard in the lobby granted you access. Perhaps over time, the guard has learned to associate you with the person you plan to visit and immediately notifies that person over the building intercom when you arrive. Argus, named after the vigilant watchman from Greek mythology, is an automated version of such a security guard: a system for automatic visitor identification. We successfully implemented and tested Argus at Just Research. To detect visitors, Argus's digital camera photographs the building entrance at regular intervals, and a motion detection algorithm identifies potential scenes containing visitors. Using a neural-network-based face detector, Argus extracts faces from these images. A memory-based face recognition system examines these faces and attempts to find visually similar matches in its stored database of visitors. An interface agent notifies system users whenever visitors arrive. Users can also provide feedback to Argus in the event of misclassified visitors. Because the face recognizer can learn online, Argus immediately incorporates these corrections into its face recognition data set.