HOG-Based Decision Tree for Facial Expression Classification

Orrite, Carlos; Gañán, Andrés; Rogez, Grégory

doi:10.1007/978-3-642-02172-5_24

Cited by 34 publications

(20 citation statements)

References 7 publications

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“…They also show a great advantage over the commonly used fiducial point-based Gabor [19], [20], [21], [22], [23], graph-based Gabor [24] and discrete Fourier transform [25] features in capturing regional information. Although other appearance-based features, such as local binary patterns (LBP) [26], [27], [10], Haar [28] and histograms of oriented gradients (HOG) [29], have shown a good performance in FER, they lack the capacity of capturing facial movement features with high accuracy. This is due to the fact that these appearance-based features are based on statistic values (e.g.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Facial Movement Features

Zhang

Tjondronegoro

2011

IEEE Trans. Affective Comput.

213

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Abstract-Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using 'salient' distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the 'salient' patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

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Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Facial Movement Features

Zhang

Tjondronegoro

2011

IEEE Trans. Affective Comput.

213

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“…The Gabor descriptor [30] is also another common feature descriptor used for FER [31], [32], [33], [34], [35]. The BRIEF descriptor [36] and HOG descriptor have been examined for use in FER as well [37], [38], [39]. In this study, we evaluate the performance of a relatively new feature descriptor called the facial landmarks descriptor [20] in facial expression recognition.…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

Real time automated facial expression recognition app development on smart phones

Alshamsi

Këpuska

Meng

2017

2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

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Abstract-Automated facial expression recognition (AFER) is a crucial technology to and a challenging task for human computer interaction. Previous methods of AFER have incorporated different features and classification methods and use basic testing approaches. In this paper, we employ the best feature descriptor for AFER by empirically evaluating the feature descriptors named the Facial Landmarks descriptor and the Center of Gravity descriptor. We examine each feature descriptor by considering one classification method, such as the Support Vector Machine (SVM) method, with three unique facial expression recognition (FER) datasets. In addition to test accuracies, we present confusion matrices of AFER. We also analyze the effect of using these feature and image resolutions on AFER performance. Our study indicates that the Facial Landmarks descriptor is the best choice to run AFER on mobile phones. The results of our study demonstrate that the proposed facial expression recognition on a mobile phone application is successful and provides up to 96.3% recognition accuracy.

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“…Proposed by Navneet Dalal and Bill Triggs in [10], HOG was originally designed for human detection. Later, HOG demonstrates its effectiveness in many computer vision tasks, such as texture classification [46], digit classification [19] and face analysis related tasks [14,52]. Based on C x and C y , magnitudes of the gradients are defined by |G| = C 2 x + C 2 y , and orientations of gradients are calculated with θ = arctan Cy Cx .…”

Section: Histogram Of Oriented Gradientsmentioning

confidence: 99%

Head pose estimation and its application in TV viewers' behavior analysis

Wu¹,

Liang²,

Ho³

2016

2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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As a reliable indicator of visual gaze direction, head pose implies a person's visual attention and interest. Therefore, head pose information extracted from face images serves as important input in many applications. In this thesis, a coarse-to-fine head pose estimation method is proposed, by decomposing the original pose space in a hierarchical structure.The estimation begins with a coarse step to identify a subspace that encompasses a set of head pose candidates. Then a subsequent fine estimation is conducted within the subspace, generating a refined result. Besides, to eliminate irrelevant information within a face image, we propose to detect Region of Interest (ROI) by exploring importance degree of image points. Furthermore, we build an application of analyzing TV viewers' behaviors from video recordings, by integrating face detection, face tracking and head pose estimation. Based on head pose and face motion, a viewer's behavior is identified to be focused or unfocused.

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HOG-Based Decision Tree for Facial Expression Classification

Cited by 34 publications

References 7 publications

Facial Expression Recognition Using Facial Movement Features

Facial Expression Recognition Using Facial Movement Features

Real time automated facial expression recognition app development on smart phones

Head pose estimation and its application in TV viewers' behavior analysis

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