Head-pose invariant facial expression recognition using convolutional neural networks

Fasel, Beat

doi:10.1109/icmi.2002.1167051

Cited by 51 publications

(42 citation statements)

References 10 publications

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“…Deep learning with convolutional neural network (CNN) such as multiscale feature based CNN [11], hierarchical committee based CNN [12] and architecture improved CNN [13] has also been applied for static expression recognition. Pramerdorfer and Kampel [14] gave a detailed survey about these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Active AU Based Patch Weighting for Facial Expression Recognition

Xie

Yang

Lai

2017

Preprint

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Abstract:Facial expression has lots of applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on Action Unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. Sparse representation based approach is then proposed to detect the active AUs of testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for Jaffe and CK+ databases, respectively. Better cross-database performance has also been observed.

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Section: Introductionmentioning

confidence: 99%

Active AU Based Patch Weighting for Facial Expression Recognition

Xie

Yang

Lai

2017

Preprint

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“…Other reports [5][6][7][8][9] on the same database did not give the recognition rate for novel expressers expression.…”

Section: Fine Classificationmentioning

confidence: 87%

“…Numerous algorithms for facial expression analysis from static images have been proposed [1,2,3] and the Japanese Female Facial Expression (JAFFE) Database is one of the common databases for testing these methods [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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A Coarse-to-Fine Classification Scheme for Facial Expression Recognition

Feng

Hadid

Pietikäinen

2004

Lecture Notes in Computer Science

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Abstract. In this paper, a coarse-to-fine classification scheme is used to recognize facial expressions (angry, disgust, fear, happiness, neutral, sadness and surprise) of novel expressers from static images. In the coarse stage, the sevenclass problem is reduced to a two-class one as follows: First, seven model vectors are produced, corresponding to the seven basic facial expressions. Then, distances from each model vector to the feature vector of a testing sample are calculated. Finally, two of the seven basic expression classes are selected as the testing sample's expression candidates (candidate pair). In the fine classification stage, a K-nearest neighbor classifier fulfils final classification. Experimental results on the JAFFE database demonstrate an average recognition rate of 77% for novel expressers, which outperforms the reported results on the same database.

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“…The recognition is performed by a two layer perceptron NN. A Convolutional NN was used in [6]. The system developed is robust to face location changes and scale variations.…”

Section: Related Workmentioning

confidence: 99%

Geometric Approach for Human Emotion Recognition using Facial Expression

Bavkar¹,

Rangole²,

Deshmukh³

2015

IJCA

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Paper contains emotion recognition system based on facial expression using Geometric approach. A human emotion recognition system consists of three steps: face detection, facial feature extraction and facial expression classification. In this paper, we used an anthropometric model to detect facial feature points. The detected feature points are group into two class static points and dynamic points. The distance between static points and dynamic points is used as a feature vector. Distance changes as we track these points in image sequence from neutral state to corresponding emotion. These distance vectors are used for input to classifier. SVM (Support Vector Machine) and RBFNN (Radial Basis Function Neural Network) used as classifier. Experimental results shows that the proposed approach is an effective method to recognize human emotions through facial expression with an emotion average recognition rate 91 % for experiment purpose the Cohn Kanade databases is used.

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Head-pose invariant facial expression recognition using convolutional neural networks

Cited by 51 publications

References 10 publications

Active AU Based Patch Weighting for Facial Expression Recognition

Active AU Based Patch Weighting for Facial Expression Recognition

A Coarse-to-Fine Classification Scheme for Facial Expression Recognition

Geometric Approach for Human Emotion Recognition using Facial Expression

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