Integrating a mixed-feature model and multiclass support vector machine for facial expression recognition

Lin, Daw-Tung; Pan, Decheng

doi:10.3233/ica-2009-0304

Cited by 35 publications

(12 citation statements)

References 33 publications

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“…Local Binary Pattern (LBP) [17] and Local Phase Quantization (LPQ) [19] are also studied to extract appearance-based facial features. Lin et al [33] proposed multistage discrimination model for facial expression recognition based on two-dimensional principal component analysis (2DPCA), and local texture represented by local binary pattern (LBP). They extensively tested their model on four databases and achieved promising results.…”

mentioning

confidence: 99%

Saliency-based framework for facial expression recognition

Khan

Meyer

Konik

et al. 2018

Front. Comput. Sci.

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mentioning

confidence: 99%

Saliency-based framework for facial expression recognition

Khan

Meyer

Konik

et al. 2018

Front. Comput. Sci.

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“…In the literature, a common approach divides the image into n non-overlapping regions [8,9,10]. Hence, we extract F LBP , F LBP u2 and F l dividing facial image into 9, 16, 25, 36, 49 and 64 non overlapping squared regions.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…In [10] the authors combine the strengths of two-dimensional principal component analysis (2DPCA) and LBP operators for feature extraction. Before LBP computation, authors apply a filter for edge detection aiming at lowering the sensitivity to noise or changes in light conditions of LBP operators, although such operators have proven their robustness to those issue [11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Facial Expression Recognition Using Statistical-Like Moments

D'Ambrosio

Iannello

Soda

2011

Image Analysis and Processing – ICIAP 2011

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Abstract. Research in automatic facial expression recognition has permitted the development of systems discriminating between the six prototypical expressions, i.e. anger, disgust, fear, happiness, sadness and surprise, in frontal video sequences. Achieving high recognition rate often implies high computational costs that are not compatible with real time applications on limited-resource platforms. In order to have high recognition rate as well as computational efficiency, we propose an automatic facial expression recognition system using a set of novel features inspired by statistical moments. Such descriptors, named as statisticallike moments extract high order statistic from texture descriptors such as local binary patterns. The approach has been successfully tested on the second edition of Cohn-Kanade database, showing a computational advantage and achieving a performance recognition rate comparable than methods based on different descriptors.

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“…yes or no); -To search for facial components such as eyes, nose, mouth, lips, and movements of these components, as for visual speech recognition [21] these activities are possible only after face detection and they can be improved with HPE; -Tasks of greater complexity, such as face authentication or recognition, and facial expression recognition [19]. There are varied methods for head pose estimation.…”

Section: Introductionmentioning

confidence: 99%

Enhanced real-time head pose estimation system for mobile device

Neto

Duarte

Barreto

et al. 2014

ICA

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This article proposes a real-time Head Pose Estimation (HPE) technique designed to be used in mobile devices. The method enables the interaction between the user and mobile devices using the device's inbuilt camera. The proposed technique is composed of different computer vision methods, which were optimized to operate in a restricted environment. The method has three Degrees of Freedom (DOF), roll, yaw and pitch. The HPE is obtained using these movements. Experiments were conducted using 363 videos of 27 different people taken in varied scenarios with changes in illumination and background. The results demonstrate the robustness and efficiency of the proposed method.

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Integrating a mixed-feature model and multiclass support vector machine for facial expression recognition

Cited by 35 publications

References 33 publications

Saliency-based framework for facial expression recognition

Saliency-based framework for facial expression recognition

Automatic Facial Expression Recognition Using Statistical-Like Moments

Enhanced real-time head pose estimation system for mobile device

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