Feature Extraction and Facial Expression Recognition Based on Bezier Curve

2014 ◽  
Author(s):  
Hong Bao ◽  
Tao Ma
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Expression Recognition

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

2013 ◽  
pp. 42-55
Author(s):  
Yi Ji ◽  
Khalid Idrissi
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Video Sequences ◽  
Polynomial Kernel ◽  
Expression Recognition ◽  
Emotional States ◽  
Small Set

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

Robust Facial Expression Recognition with Low-Rank Sparse Error Dictionary Based Probabilistic Collaborative Representation Classification

2017 ◽  
Vol 26 (04) ◽  
pp. 1750017 ◽  
Author(s):  
Zhe Sun ◽  
Zheng-Ping Hu ◽  
Meng Wang ◽  
Fan Bai ◽  
Bo Sun
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Random Noise ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Low Rank ◽  
Collaborative Representation ◽  
Expression Recognition ◽  
Maximum Probability

The performance of facial expression recognition (FER) would be degraded due to some factors such as individual differences, Gaussian random noise and so on. Prior feature extraction methods like Local Binary Patterns (LBP) and Gabor filters require explicit expression components, which are always unavailable and difficult to obtain. To make the facial expression recognition (FER) more robust, we propose a novel FER approach based on low-rank sparse error dictionary (LRSE) to remit the side-effect caused by the problems above. Then the query samples can be represented and classified by a probabilistic collaborative representation based classifier (ProCRC), which exploits the maximum likelihood that the query sample belonging to the collaborative subspace of all classes can be better computed. The final classification is performed by seeking which class has the maximum probability. The proposed approach which exploits ProCRC associated with the LRSE features (LRSE ProCRC) for robust FER reaches higher average accuracies on the different databases (i.e., 79.39% on KDEF database, 89.54% on CAS-PEAL database, 84.45% on CK+ database etc.). In addition, our method also leads to state-of-the-art classification results from the aspect of feature extraction methods, training samples, Gaussian noise variances and classification based methods on benchmark databases.

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