Fusion of feature sets and classifiers for facial expression recognition

Zavaschi, Thiago H. H.; Britto, Alceu S.; Oliveira, Luiz S.; Koerich, Alessandro L.

doi:10.1016/j.eswa.2012.07.074

Cited by 111 publications

(56 citation statements)

References 34 publications

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“…Thiago et al utilize Gabor and LBP as feature descriptors. Their experiments on the Cohn-Kanade and Japanese Female Facial Expression (JAFFE) datasets showed that the recognition rate with multiple features and classifiers is 10% and 5% above that with a single feature and classifier respectively [26]. Koelstra et al propose a dynamic texture-based approach based on facial Action Units (AUs).…”

Section: Fusion Strategy For Facial Expression Recognitionmentioning

confidence: 99%

“…The accuracy rate achieved in our proposed approach is highlighted in bold. [39] 88.9% Boosted-LBP + SVM Thiago et al (2013) [26] 88.9% Ensemble Gabor and LBP Xu et al (2009) [40] 89.1% ASM + geometry feature + LSVM classifier Jia et al (2013) [29] 90.7% Dynamic geometry feature + stacking Tian Y et al (2002) [3] 92.7% Gabor wavelets + geometry with FACS Chen-Chiung Hsieh et al (2016) [31] 94.7% dynamic face regions + A multi-class SVM Deepak Ghimire et al (2013) [23] 95.17% Feature selective multi-class AdaBoost Our proposed approach 96.62% Multi-layer of optimally weighted AU Ping et al (2014) [41] 96.7% Boosted deep belief network Deepak Ghimire et al (2013) [23] 97.35% SVM on boosted features The results in Tables 11 and 12 show that our proposed fusion method outperforms the others in both CK and JAFFE datasets. Comparing with the deep belief network of Ping et al [41], our proposed method achieves a comparative recognition rate only 0.1% less than the current prevalent deep learning based method.…”

Section: Comparison With Several State-of-the-art Expression Recognitmentioning

confidence: 99%

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A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

et al. 2017

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Abstract:Affective computing is an increasingly important outgrowth of Artificial Intelligence, which is intended to deal with rich and subjective human communication. In view of the complexity of affective expression, discriminative feature extraction and corresponding high-performance classifier selection are still a big challenge. Specific features/classifiers display different performance in different datasets. There has currently been no consensus in the literature that any expression feature or classifier is always good in all cases. Although the recently updated deep learning algorithm, which uses learning deep feature instead of manual construction, appears in the expression recognition research, the limitation of training samples is still an obstacle of practical application. In this paper, we aim to find an effective solution based on a fusion and association learning strategy with typical manual features and classifiers. Taking these typical features and classifiers in facial expression area as a basis, we fully analyse their fusion performance. Meanwhile, to emphasize the major attributions of affective computing, we select facial expression relative Action Units (AUs) as basic components. In addition, we employ association rules to mine the relationships between AUs and facial expressions. Based on a comprehensive analysis from different perspectives, we propose a novel facial expression recognition approach that uses multiple features and multiple classifiers embedded into a stacking framework based on AUs. Extensive experiments on two public datasets show that our proposed multi-layer fusion system based on optimal AUs weighting has gained dramatic improvements on facial expression recognition in comparison to an individual feature/classifier and some state-of-the-art methods, including the recent deep learning based expression recognition one.

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Section: Fusion Strategy For Facial Expression Recognitionmentioning

confidence: 99%

Section: Comparison With Several State-of-the-art Expression Recognitmentioning

confidence: 99%

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

et al. 2017

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“…Aiming to describe the different expressions more effectively, diverse features extracted methods are used in facial expression recognition. Typical hand-crafted features include Local Binary Patterns (LBP) [4],Histogram of Oriented Gradient (HOG) [8], Scale Invariant Feature Transform (SIFT) [16], and the fusion of these features [7]. According to these literature, the fusion features contain more information about these expressions and achieve better results.…”

Section: Introductionmentioning

confidence: 99%

“…Likes other recognition research, facial expressions recognition uses data from videos, images sequences [6] and static images [3,7]. Whole movement processes of the expressions are applied in the researches which use videos and images.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas

Liu

et al. 2017

Preprint

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Abstract:In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features' dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

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“…Processing the images that obtained from the videos has been used in different applications such as surveillance, action recognition, tracking, face detection and emotion recognition [4,5]. Due to the emergence of advantaged technologies, these applications are integrated with some electronic devices for sharing the mood and perception of people [6]. So, the analysis of people's mood detection is a main role in video processing.…”

Section: Introductionmentioning

confidence: 99%

CLBPGNN Convoluted Local Binary Pattern based Grouping Neural Network for Face Emotion Recognition

G.¹,

Krithika²

2017

JESTR

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Due to the variations of facial expressions, the process of emotion recognition is a highly demanding and challenging task in many image processing applications. So, the traditional works introduced different techniques for facial image recognitions, which includes the drawbacks of reduced accuracy and recognition rate. Thus, this work aims to develop a new emotion recognition system by implementing different image processing techniques. Initially, the image obtained from the video sequence is preprocessed by employing the Neighborhood Pixel Intensity based Filtering (NPIF) technique. Then, the Viola Jones algorithm is implemented to extract the face by suppressing the background and foreground regions, which increases the accuracy of recognition. After that, the Convoluted Local Binary Pattern (CLBP) technique is employed to extract the patterns of the background suppressed image. Based on the extracted key points, the emotion of the given image is recognized by using the Grouping based Neural Network (GNN) classification technique.

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Fusion of feature sets and classifiers for facial expression recognition

Cited by 111 publications

References 34 publications

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

A Multi-Layer Fusion-Based Facial Expression Recognition Approach with Optimal Weighted AUs

Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas

CLBPGNN Convoluted Local Binary Pattern based Grouping Neural Network for Face Emotion Recognition

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