Facial Expression Recognition via a Boosted Deep Belief Network

Liu, Ping; Han, Shizhong; Meng, Zibo; Tong, Yan

doi:10.1109/cvpr.2014.233

Cited by 553 publications

(284 citation statements)

References 28 publications

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“…For Jaffe database, our proposed algorithm achieves competitive recognition rate among all the algorithms in Table 6. The algorithm [41] using deep belief network yields the highest recognition rate of 91.8%. However, feature selection and classifier training are time-consuming and the process requires several days for each database.…”

Section: Comparison With State Of the Artmentioning

confidence: 99%

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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: Comparison With State Of the Artmentioning

confidence: 99%

“…For the CK+ database, the proposed algorithm achieves the highest accuracy of 94.09%. As we are focused on seven class expression recognition, those work developed for six expressions like [21,37,41,[43][44][45]49] are not included for comparison in this paper.…”

Section: Comparison With State Of the Artmentioning

confidence: 99%

Active AU Based Patch Weighting for Facial Expression Recognition

Xie

Yang

Lai

2017

Preprint

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“…Many existing facial behavior recognition methods use still images where each characteristic face is captured at the apex (i.e., the exact moment where facial and head motion is exerted most) [4]- [6]. However, psychological studies [7] have shown that video recognition enables more accurate and robust recognition of facial expressions.…”

Section: Related Work and Problem Contextmentioning

confidence: 99%

Natural Facial and Head Behavior Recognition using Dictionary of Motion Primitives

Shi

Ukita

Yang

2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a natural facial and head behavior recognition method using hybrid dynamical systems. Most existing facial and head behavior recognition methods focus on analyzing deliberately displayed prototypical emotion patterns rather than complex and spontaneous facial and head behaviors in natural conversation environments. We first capture spatio-temporal features on important facial parts via dense feature extraction. Next, we cluster the spatio-temporal features using hybrid dynamical systems, and construct a dictionary of motion primitives to cover all possible elemental motion dynamics accounting for facial and head behaviors. With this dictionary, the facial and head behavior can be interpreted into a distribution of motion primitives. This interpretation is robust against different rhythms of dynamic patterns in complex and spontaneous facial and head behaviors. We evaluate the proposed approach under natural tele-communication scenarios, and achieve promising results. Furthermore, the proposed method also performs favorably against the state-ofthe-art methods on three benchmark databases.

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“…Neural networks are now commonly used in complex classification tasks such as image recognition (e.g., Krizhevsky et al 2012;Liu et al 2014;Wang et al 2016;Shen et al 2015), speech and music recognition (e.g., Hung et al 2005;Jaitly & Hinton 2011;Zhang & Wu 2013;Pradeep & Kumaraswamy 2014), biology (e.g., Head-Gordon & Stillinger 1993Plebe 2007;Wu & McLarty 2012;Spencer et al 2015), and are finding increased use in the classification of galaxies and cosmology (e.g., Collister & Lahav 2004;Agarwal et al 2012Agarwal et al , 2014Reis et al 2012;Karpenka et al 2013;Dieleman et al 2015;du Buisson et al 2015;Ellison et al 2015;Huertas-Company et al 2015).…”

Section: Robertmentioning

confidence: 99%

Dreaming of Atmospheres

Waldmann

2016

ApJ

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Here, we introduce the RobERt(Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/ atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERtalgorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the "dreams" of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.

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Facial Expression Recognition via a Boosted Deep Belief Network

Cited by 553 publications

References 28 publications

Active AU Based Patch Weighting for Facial Expression Recognition

Active AU Based Patch Weighting for Facial Expression Recognition

Natural Facial and Head Behavior Recognition using Dictionary of Motion Primitives

Dreaming of Atmospheres

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