Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Gaze Tracking for Multimodal Human-Computer Interaction
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Head Pose Estimation using Fisher Manifold Learning
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Robust Real-Time Face Detection
International Journal of Computer Vision
Head Pose Estimation by Nonlinear Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Tracking the multi person wandering visual focus of attention
Proceedings of the 8th international conference on Multimodal interfaces
Locality discriminating indexing for document classification
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Estimating face pose by facial asymmetry and geometry
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Visual focus of attention recognition in the ambient kitchen
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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In this paper, we present a system to monitor a subject's Visual Focus of Attention (VFOA) based on his/her head poses. The system first detects faces from video images and determines if the detected face is a frontal or profile face. If a frontal face is detected, the system further estimates the head pose from the face image. Instead of estimating accurate head poses through detection or tracking methods, we formulate the problem as a classification problem and classify the head pose into one of a predefined number of poses using a local discriminant projection (LDP) method. The LDP method uses two graphs for the modeling the head pose embedding, one is the nearest native neighbor graph, the other is the nearest invader graph. We evaluate the LDP method in CAS-PEAL Database with 21 head poses and a realistic data set with 9 poses collected from our application scenario. The experimental results indicate that our approach outperforms other methods. We describe the implementation of the system with an application in monitoring customers' VFOA in a display window that exhibits merchandise in a shop. The system can be used to index and retrieve information for customer analysis.