A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
A fast algorithm for tracking human faces based on chromatic histograms
Pattern Recognition Letters
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Experiments on Eigenfaces Robustness
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A hidden markov model-based approach for face detection and recognition
A hidden markov model-based approach for face detection and recognition
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Open Set Face Recognition Using Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Journal of Cognitive Neuroscience
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Person re-identification in TV series using robust face recognition and user feedback
Multimedia Tools and Applications
Hi-index | 0.00 |
In this paper, we present a real-time video-based face recognition system. The developed system identifies subjects while they are entering a room. This application scenario poses many challenges. Continuous, uncontrolled variations of facial appearance due to illumination, pose, expression, and occlusion of non-cooperative subjects need to be handled to allow for successful recognition. In order to achieve this, the system first detects and tracks the eyes for proper registration. The registered faces are then individually classified by a local appearance-based face recognition algorithm. The obtained confidence scores from each classification are progressively combined to provide the identity estimate of the entire sequence. We introduce three different measures to weight the contribution of each individual frame to the overall classification decision. They are distance-to-model (DTM), distance-to-second-closest (DT2ND), and their combination. We have conducted closed-set and open-set identification experiments on a database of 41 subjects. The experimental results show that the proposed system is able to reach high correct recognition rates. Besides, it is able to perform facial feature and face detection, tracking, and recognition in real-time.