ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Robust Real-Time Face Detection
International Journal of Computer Vision
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
2D and 3D face recognition: A survey
Pattern Recognition Letters
Manifold Learning for Gender Classification from Face Sequences
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Model Based Analysis of Face Images for Facial Feature Extraction
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Spatio-temporal Facial Features for HRI Scenarios
CRV '11 Proceedings of the 2011 Canadian Conference on Computer and Robot Vision
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Human faces are 3D complex objects consisting of geometrical and appearance variations. They exhibit local and global variations when observed over time. In our daily life communication, human faces are seen in actions conveying a set of information during interaction. Cognitive science explains that human brains are capable of extracting this set of information very efficiently resulting in a better interaction with others. Our goal is to extract a single feature set which represents multiple facial characteristics. This problem is addressed by the analysis of different feature components on facial classifications using a 3D surface model. We propose a unified framework which is capable to extract multiple information from the human faces and at the same time robust against rigid and non-rigid facial deformations. A single feature vector corresponding to a given image is representative of person's identity, facial expressions, gender and age estimation. This feature set is called spatio-temporal multifeature (STMF) extracted from image sequences. An STMF is configured with three different feature components which is tested thoroughly to evidence its validity. The experimental results from four different databases show that this feature set provides high accuracy and at the same time exhibits robustness. The results have been discussed comparatively with different approaches.