Intelligent pixels of interest selection with application to facial expression recognition using multilayer perceptron

Authors:
Taner Danisman;Ioan Marius Bilasco;Jean Martinet;Chabane Djeraba
Affiliations:
Lille University of Science and Technology, CNRS UMR 8022, Parc Scientifique de la Haute Borne, 50 Avenue Halley, 59655 Villeneuve D'Ascq, France;Lille University of Science and Technology, CNRS UMR 8022, Parc Scientifique de la Haute Borne, 50 Avenue Halley, 59655 Villeneuve D'Ascq, France;Lille University of Science and Technology, CNRS UMR 8022, Parc Scientifique de la Haute Borne, 50 Avenue Halley, 59655 Villeneuve D'Ascq, France;Lille University of Science and Technology, CNRS UMR 8022, Parc Scientifique de la Haute Borne, 50 Avenue Halley, 59655 Villeneuve D'Ascq, France
Venue:
Signal Processing
Year:
2013

Citing 22
Cited 0

Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

Machine Learning
Neural Network-Based Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: algorithms and applications

Neural Networks
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis for Recognition of Human Face Images (Invited Paper)

AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Robust Real-Time Face Detection

International Journal of Computer Vision
Eigenfaces for recognition

Journal of Cognitive Neuroscience
An analysis of facial expression recognition under partial facial image occlusion

Image and Vision Computing
Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition

Signal Processing
Locating Facial Features with an Extended Active Shape Model

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Combining appearance and motion for face and gender recognition from videos

Pattern Recognition
Projected gradient method for kernel discriminant nonnegative matrix factorization and the applications

Signal Processing
Image ratio features for facial expression recognition application

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Local appearance based face recognition method using block based steerable pyramid transform

Signal Processing
Visual-context boosting for eye detection

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input variable selection for feature extraction in classification problems

Signal Processing
Graph-Preserving Sparse Nonnegative Matrix Factorization With Application to Facial Expression Recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent

IEEE Transactions on Image Processing
3D face recognition using local binary patterns

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper presents an automatic way to discover pixels in a face image that improves the facial expression recognition results. Main contribution of our study is to provide a practical method to improve classification performance of classifiers by selecting best pixels of interest. Our method exhaustively searches for the best and worst feature window position from a set of face images among all possible combinations using MLP. Then, it creates a non-rectangular emotion mask for feature selection in supervised facial expression recognition problem. It eliminates irrelevant data and improves the classification performance using backward feature elimination. Experimental studies on GENKI, JAFFE and FERET databases showed that the proposed system improves the classification results by selecting the best pixels of interest.