IEEE Intelligent Systems
DESEO: An Active Vision System for Detection, Tracking and Recognition
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Automatic Video-based Person Authentication Using the RBF Network
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Towards unconstrained face recognition from image sequences
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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
Recognizing Faces in Broadcast Video
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Detection of Frontal Faces in Video Streams
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
Identity Management in Face Recognition Systems
Biometrics and Identity Management
| Hi-index | 0.00 |
In face recognition, where high-dimensional representation spaces are generally used, it is very important to take advantage of all the available information. In particular, many labelled facial images will be accumulated while the recognition system is functioning, and due to practical reasons some of them are often discarded. In this paper, we propose an algorithm for using this information. The algorithm has the fundamental characteristic of being incremental. On the other hand, the algorithm makes use of a combination of classification results for the images in the input sequence. Experiments with sequences obtained with a real person detection and tracking system allow us to analyze the performance of the algorithm, as well as its potential improvements.