Real time facial expression recognition in video using support vector machines

Michel, P.; Kaliouby, Rana el

doi:10.1145/958468.958479

Cited by 142 publications

(81 citation statements)

References 11 publications

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“…Points based approaches use fiducial points for shape representation. Michel et al [33] use a tracker to get 22 fiducial points and calculate the distance of each point between a neutral and a peak frame. These distances are used as features of an Support Vector Machine (SVM) algorithm in order classify the emotions.…”

Section: Facial Emotion Recognition Approachesmentioning

confidence: 99%

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Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

Montenegro¹,

Gkelias

Argyriou³

2017

Lecture Notes in Computer Science

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Abstract. Alzheimer Disease (AD) early detection is considered of high importance for improving the quality of life of patients and their families. Amongst all the different approaches for AD detection, significant work has been focused on emotion analysis through facial expressions, body language or speech. Many studies also use the electroencephalogram in order to capture emotions that patients cannot physically express. Our work introduces an emotion recognition approach using facial expression and EEG signal analysis. A novel dataset was created specifically to remark the autobiographical memory deficits of AD patients. This work uses novel EEG features based on quaternions, facial landmarks and the combination of them. Their performance was evaluated in a comparative study with a state of the art methods that demonstrates the proposed approach.

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Section: Facial Emotion Recognition Approachesmentioning

confidence: 99%

“…Once the data was collected and preprocessed, the features were extracted. The spatio-temporal facial features studied were based on Michel et al work [33]. The distance of each coordinate to the nose point was measured.…”

Section: Emotion Recognition Using Fiducial Points and Eeg Quaternionmentioning

confidence: 99%

Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

Montenegro¹,

Gkelias

Argyriou³

2017

Lecture Notes in Computer Science

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“…We used similar methodology developed by Wimmer et al [4] which combines multitude of qualitatively different features [19], determines the most relevant features using machine learning and learns objective functions from annotated images [18]. To extract discriptive features from the image, Michel et al [14] extracted the location of 22 feature points within the face and determine their motion between an image that shows the neutral state of the face and an image that represents a facial expression. The very similar approach of Cohn et al [15] uses hierarchical optical flow in order to determine the motion of 30 feature points.…”

Section: Related Workmentioning

confidence: 99%

“…This approach is applied by Kotsia et al [16] to design Support Vector Machines (SVM) for classification. Michel et al [14] train a Support Vector Machine (SVM) that determines the visible facial expression within the video sequences of the Cohn-Kanade Facial Expression Database by comparing the first frame with the neutral expression to the last frame with the peak expression. In order to perform face recognition applications many researchers have applied model based approaches.…”

Section: Related Workmentioning

confidence: 99%

Model Based Analysis of Face Images for Facial Feature Extraction

Riaz

Mayer

Beetz

et al. 2009

Computer Analysis of Images and Patterns

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Abstract. This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with binary decision tree (BDT) and Bayesian Network (BN) for classification. We evaluated our results on image sequences of Cohn Kanade Facial Expression Database (CKFED). The proposed system produced very promising recognition rates for our applications with same set of features and classifiers. The system is also realtime capable and automatic.

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“…The second step in the procedure consists in defining a decision/classification rule which associates the feature-based representation with the correct facial expression. Previous works used for this step several well-know methods: HMMbased classifier [12], template matching [18], SVM [15], Dynamic Bayesian Networks [9]. The standard hard classification approach associates any two examples having the same features to the same corresponding class.…”

Section: Introductionmentioning

confidence: 99%