Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The M2VTS Multimodal Face Database (Release 1.00)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
An efficient discriminant-based solution for small sample size problem
Pattern Recognition
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition
Computers & Mathematics with Applications
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Hi-index | 12.05 |
Visual lip-reading systems can enhance the speech recognition systems accuracy. Performance of lip-reading systems is not high in comparison with audio speech recognition systems due to overlap of patterns of classes and outliers. Thus, lip reading is complex classification problem which can be solved efficiently using ensemble methods. Multi-linear-Discriminant Analysis (MLDA) is a recently proposed method which has good classification performance on the face recognition problem. In this study, a new method of boosting algorithm based on MLDA and nearest neighbor is proposed for lip reading problems. Additionally, to enhance the classification accuracy a new feature extraction and combination techniques are proposed which can extract useful feature from lip reading image databases. Extracted features of samples are encoded as tensor objects to feed in MLDA learner of the boosting method. Empirical evaluation of the novel boosting method and feature extraction techniques on the M2VTS image database reveals excellent result with respect to other linear and multi-linear algorithms.