Increasing attraction of pseudo-inverse autoassociative networks
Neural Processing Letters
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection From Color Images Using a Fuzzy Pattern Matching Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Modal Tracking of Faces for Video Communications
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
On Importance of Nose for Face Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Identity Management in Face Recognition Systems
Biometrics and Identity Management
Subclass linear discriminant analysis for video-based face recognition
Journal of Visual Communication and Image Representation
Robust Human Face Detection for Moving Pictures Based on Cascade-Typed Hybrid Classifier
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
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There is a physiological reason, backed up by the theory of visual attention in living organisms, why animals look into each others' eyes. This is to illustrate the main two properties in which recognizing of faces in video differs from its static counterpart - recognizing of faces in images. First, the lack of resolution in video is abundantly compensated by the information coming from the time dimension. Video data is inherently of a dynamic nature. Second, video processing is a phenomena occurring all the time around us - in biological systems, and many results unraveling the intricacies of biological vision already obtained. At the same time, as we examine the way the video-based face recognition is approached by computer scientists, we notice that up till now video information is often used partially and therefore not very efficiently. This work aims at bridging this gap. We develop a multi-channel framework for video-based face processing, which incorporates the dynamic component of video. The utility of the framework is shown on the example of detecting and recognizing faces from blinking. While doing that we derive a canonical representation of a face best suited for the task.