Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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
Face Image Retrieval Using HMMs
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Towards unconstrained face recognition from image sequences
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Head Pose Estimation Using View Based Eigenspaces
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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
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
Facial image classification of mouse embryos for the animal model study of fetal alcohol syndrome
Proceedings of the 2009 ACM symposium on Applied Computing
Expression-invariant face recognition with constrained optical flow warping
IEEE Transactions on Multimedia
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
A new multi-purpose audio-visual UNMC-VIER database with multiple variabilities
Pattern Recognition Letters
Hi-index | 0.00 |
To date, advances in face recognition have been dominated by the design of algorithms that do recognition from a single test image. Recently, an obvious but important question has been put forward. Will the recognition results of such approaches be generally improved when using multiple images or video sequences? To test this, we extend the formulation of a probabilistic appearance-based face recognition approach (which was originally defined to do recognition from a single still) to work with multiple images and video sequences. In our algorithm, as it is the case in most appearance-based approaches, we will need to use a feature extraction algorithm to find those features that best describe and discriminate among face images of distinct people. We will show that regardless of the algorithm used, the recognition results improve considerably when one uses a video sequence rather than a single still. Hence, a positive answer to our question (in the general sense) seems reasonable. The probabilistic algorithm we propose in this paper is robust to partial occlusions, orientation and expression changes, and does not require of a precise localization of the face or facial features. We will also show how these problems are more easily solved when one uses a video sequence rather than a single image for testing. The limitations of our algorithm will also be discussed. Understanding the limitations of current techniques when applied to video is important, because it helps identify those weak points that require further consideration.