Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
PCA = Gabor for Expression Recognition
PCA = Gabor for Expression Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Fast Asymmetric Learning for Cascade Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial affect recognition using regularized discriminant analysis-based algorithms
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Local 3D Shape Analysis for Facial Expression Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Feature level analysis for 3D facial expression recognition
Neurocomputing
Facial expression recognition using constructive feedforward neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Least-squares image resizing using finite differences
IEEE Transactions on Image Processing
| Hi-index | 12.05 |
This study improves the recognition accuracy and execution time of facial expression recognition system. Various techniques were utilized to achieve this. The face detection component is implemented by the adoption of Viola-Jones descriptor. The detected face is down-sampled by Bessel transform to reduce the feature extraction space to improve processing time then. Gabor feature extraction techniques were employed to extract thousands of facial features which represent various facial deformation patterns. An AdaBoost-based hypothesis is formulated to select a few hundreds of the numerous extracted features to speed up classification. The selected features were fed into a well designed 3-layer neural network classifier that is trained by a back-propagation algorithm. The system is trained and tested with datasets from JAFFE and Yale facial expression databases. An average recognition rate of 96.83% and 92.22% are registered in JAFFE and Yale databases, respectively. The execution time for a 100x100 pixel size is 14.5ms. The general results of the proposed techniques are very encouraging when compared with others.