Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Expression Recognition for E-learning Systems using Gabor Wavelet & Neural Network
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
An analysis of facial expression recognition under partial facial image occlusion
Image and Vision Computing
Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
Averaged Gabor Filter Features for Facial Expression Recognition
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
A novel fusion-based method for expression-invariant gender classification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 02
Face and facial expression recognition with an embedded system for human-robot interaction
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
A real-time automated system for the recognition of human facial expressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Bee royalty offspring algorithm for improvement of facial expressions classification model
International Journal of Bio-Inspired Computation
| Hi-index | 0.00 |
Facial expression recognition has recently become an important research area, and many efforts have been made in facial feature extraction and its classification to improve face recognition systems. Most researchers adopt a posed facial expression database in their experiments, but in a real-life situation the facial expressions may not be very obvious. This article describes the extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions. The objective of our research was to investigate the performance of a facial expression recognition system with a minimum number of features of the Gabor wavelet. In this research, principal component analysis (PCA) is employed to compress the Gabor features. We also discuss the selection of the minimum number of Gabor features that will perform the best in a recognition task employing a multiclass support vector machine (SVM) classifier. The performance of facial expression recognition using our approach is compared with those obtained previously by other researchers using other approaches. Experimental results showed that our proposed technique is successful in recognizing natural facial expressions by using a small number of Gabor features with an 81.7% recognition rate. In addition, we identify the relationship between the human vision and computer vision in recognizing natural facial expressions.