Static and dynamic 3D facial expression recognition: A comprehensive survey

Sandbach, Georgia; Zafeiriou, Stefanos; Pantić, Maja; Liu, Yin

doi:10.1016/j.imavis.2012.06.005

Cited by 328 publications

(173 citation statements)

References 105 publications

(215 reference statements)

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“…Thus, p f p k is the output feature map with value ranging from 0 to 15, which is further transformed to the histogram for feature representation. For each face patch or region, the corresponding feature GSF is then vectorized as a 16 × n s × n a dimension vector, where n s , n a are defined in Equation (1). Finally, the feature of the i-th expression sample is represented as…”

Section: Feature Extractionmentioning

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: Feature Extractionmentioning

confidence: 99%

Active AU Based Patch Weighting for Facial Expression Recognition

Xie

Yang

Lai

2017

Preprint

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“…The image-transform-based descriptors, i.e., PCA, DCT and DWT [36], are the most commonly used feature representations for visual speech processing tasks [50], [51]. LBP has been widely used as a robust image compression technique for texture representation [37], and it is one of the most commonly used facial appearance descriptors in face recognition and facial expression recognition [39], [40]. HOG was first applied for human detection in images [38], but has proved successful in a wide range of computer vision problems, like recently in lip activity analysis [52].…”

Section: ) Mouth Region Of Interest (Roi) Extractionmentioning

confidence: 99%

Discrimination Between Native and Non-Native Speech Using Visual Features Only

Georgakis

Petridis

Pantić

2016

IEEE Trans. Cybern.

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Abstract-Accent is a soft biometric trait that can be inferred from pronunciation and articulation patterns characterising the speaking style of an individual. Past research has addressed the task of classifying accent, as belonging to a native language speaker or a foreign language speaker, by means of the audio modality only. However, features extracted from the visual stream of speech have been successfully used to extend or substitute audio-only approaches that target speech or language recognition. Motivated by these findings, we investigate to what extent temporal visual speech dynamics attributed to accent can be modelled and identified when the audio stream is missing or noisy, and the speech content is unknown. We present here a fully automated approach to discriminating native from non-native English speech, based exclusively on visual cues. A systematic evaluation of various appearance and shape features for the target problem is conducted, with the former consistently yielding superior performance. Subject-independent cross-validation experiments are conducted on mobile phone recordings of continuous speech and isolated word utterances spoken by 56 subjects from the challenging MOBIO Database. High performance is achieved on a text-dependent protocol, with the best score of 76.5% yielded by fusion of five HMMs trained on appearance features. Our framework is also efficient even when tested on examples of speech unseen in the training phase, although performing less accurately compared to the text-dependent case.

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“…A model for emotion recognition using facial Points Localization Model has been developed in [4]. Sandbach made a comprehensive survey on the developments of 3D and 4D facial expression recognition, and reviewed the tracking and alignment methods [5]. A novel phase congruency based descriptor for dynamic facial expression analysis which is robust to image scale and illumination variations was introduced in [6].…”

Section: Introductionmentioning

confidence: 99%

Geometrical Approach for Emotion Recognition from Facial Expressions Using 4D Videos and Analysis on Feature-Classifier Combination

Palaniswamy¹,

Tripathi²

2017

IJIES

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Emotion recognition from facial expressions using videos is important in human computer communication where the continuous changes in face movements need to be recognized efficiently. In this paper, a method using the geometrical based approach for feature extraction and recognition of six basic emotions has been proposed which is named as GAFCI (Geometrical Approach for Feature Classifier Identification). Various classifiers, Support Vector Machine (SVM), Random Forest, Naïve Bayes and Neural Networks are used for classification, and the performances of all the chosen classifiers are compared. Out of the 83 feature points provided in the BU4DFE database, optimum feature points are identified by experimenting with several sets of feature points. Suitable "feature-classifier" combination has been obtained by varying the number of feature points, classifier parameters, and training and test samples. A detailed analysis on the feature points and classifiers has been performed to learn the relationship between distance parameters and classification of emotions. The results are compared with literature and found to be encouraging.

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Static and dynamic 3D facial expression recognition: A comprehensive survey

Cited by 328 publications

References 105 publications

Active AU Based Patch Weighting for Facial Expression Recognition

Active AU Based Patch Weighting for Facial Expression Recognition

Discrimination Between Native and Non-Native Speech Using Visual Features Only

Geometrical Approach for Emotion Recognition from Facial Expressions Using 4D Videos and Analysis on Feature-Classifier Combination

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