Facial Expression Recognition Using Dual Layer Hierarchical SVM Ensemble Classification

Mariappan, Mahesh Babu; Suk, Myunghoon; Prabhakaran, Balakrishnan

doi:10.1109/ism.2012.104

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…After that, ensemble learning has attracted many people's attention. It can learn groups of algorithms in some way, then it significant increases the generalization ability of machine learning algorithm [14,15], and enhances the accuracy and stability of the predict results. Its advantage makes it become the head of machine learning research rapidly and the focus in international machine learning research.…”

Section: Ensemble Learningmentioning

confidence: 99%

Feature Extraction for Facial Expression Recognition based on Ensemble Learning Algorithm

Gong¹,

Chen²,

Jia³

2015

IJHIT

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The accuracy of facial expression feature extraction directly influences the recognition rate of facial expression. In order to extract facial expression feature effectively, this paper puts forward a new way of facial expression feature extraction ensemble learning algorithm based on ensemble thinking. The superposition method of heteromorphy ensemble learning is used to construct an ensemble learning model, two-d gabor wavelet, block local binary patterns and two-directional two-dimensional principal component analysis as the single learning device. Firstly, two-d gabor wavelet was used to get image texture information at each level. Then the image was divided into some blocks to acquire the eigenvectors of block local binary patterns, and reduced its dimensionality. Next, the dimensionality was further reduced by two-directional two-dimensional principal component analysis, extracted the effective characteristic features at the same time. Finally, it is classified by nearest neighbor classifier on the extracted feature library. Experimental results on JAFFE expression database show that this ensemble learning model gets higher recognition rate and better generalization ability than single learning device.

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Section: Ensemble Learningmentioning

confidence: 99%

Feature Extraction for Facial Expression Recognition based on Ensemble Learning Algorithm

Gong¹,

Chen²,

Jia³

2015

IJHIT

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“…The classification is made by SRC. SVM ensemble classification for facial expression recognition is described in [12]. From the facial images, features are extracted by base level feature extraction technique.…”

Section: Introductionmentioning

confidence: 99%

Multiple Instance Learning for Human Emotion Analysis Using Gabor Features

Nagarajan¹

2018

International Journal of Advances in Signal and Image Sciences

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Facial expression analysis (FEA) or Human Emotion Analysis (HEA) is an essential tool for human computer interaction. The nonverbal messages of humans are expressed by facial expression. In this study, an HEA system to classify seven classes of human emotions like happy, sad, angry, disgust, fear, surprise and neutral is presented. It uses Gabor filter for feature extraction and Multiple Instance Learning (MIL) for classification. Gabor filter analyzes the facial images in a localized region to extract specific frequency content in specific directions. Then, MIL classifier is used for the classification of emotions into any one of the seven emotions. The evaluation of HEA system is carried on JApanese Female Facial Expression (JAFFE) database. The overall recognition rate of the HEA system using Gabor and MIL technique is 95%.

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“…Accordingly, this work implements a HL-FER that is capable of performing accurate facial expression recognition across multiple datasets. Previously, such model has been used in [ 65 ] that was dual-layer SVM ensemble classification. The motivation behind their study was to determine how the contraction of muscles changes the appearance of the face by extracting the local features from the three parts of the face such as mouth, nose, and eyes.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

Ṣiddiqi

Lee

et al. 2013

Sensors

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Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.

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Facial Expression Recognition Using Dual Layer Hierarchical SVM Ensemble Classification

Cited by 5 publications

References 13 publications

Feature Extraction for Facial Expression Recognition based on Ensemble Learning Algorithm

Feature Extraction for Facial Expression Recognition based on Ensemble Learning Algorithm

Multiple Instance Learning for Human Emotion Analysis Using Gabor Features

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

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