Book Chapter10.1007/978-0-387-73003-5_98
Facial Expression Recognition
Maja Pantic
- 20 Jul 2009
- pp 400-406
TL;DR: Facial expression recognition is a process performed by humans or computers that consists of analyzing the motion of facial features and/or the changes in the appearance of facial Features and classifying this information into some facialexpression-interpretative categories such as facial muscle activations.
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Abstract: Facial expression recognition is a process performed by humans or computers, which consists of:
1. Locating faces in the scene (e.g., in an image; this step is also referred to as face detection),
2. Extracting facial features from the detected face region (e.g., detecting the shape of facial components or describing the texture of the skin in a facial area; this step is referred to as facial feature extraction),
3. Analyzing the motion of facial features and/or the changes in the appearance of facial features and classifying this information into some facialexpression-interpretative categories such as facial muscle activations like smile or frown, emotion (affect) categories like happiness or anger, attitude categories like (dis)liking or ambivalence, etc. (this step is also referred to as facial expression interpretation).
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Citations
Emotion-Induced Engagement in Internet Video Advertisements
TL;DR: In this paper, a controlled experiment was conducted to assess the effect of emotion and surprise on the concentration of attention and viewer retention by recording zapping behavior in Internet video advertisements, showing that surprise and joy effectively concentrate attention and retain viewers, whereas the level rather than the velocity of surprise affects attention concentration most.
413
Facial Expression Recognition with Inconsistently Annotated Datasets
Jiabei Zeng,Shiguang Shan,Xilin Chen +2 more
- 08 Sep 2018
TL;DR: This work proposes an Inconsistent Pseudo Annotations to Latent Truth (IPA2LT) framework to train a FER model from multiple inconsistently labeled datasets and large scale unlabeled data and shows that the method outperforms other state-of-the-art and optional methods under a rigorous evaluation protocol involving 7 FER datasets.
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition
TL;DR: This paper proposes to bridge the emotional gap by using a hybrid deep model, which first produces audio–visual segment features with Convolutional Neural Networks and 3D-CNN, then fuses audio– visual segment features in a Deep Belief Networks (DBNs).
346
Local Learning With Deep and Handcrafted Features for Facial Expression Recognition
Mariana-Iuliana Georgescu,Radu Tudor Ionescu,Marius Popescu +2 more
- 16 May 2019
TL;DR: Zhang et al. as discussed by the authors proposed an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve the state of the art results in facial expression recognition (FER).
From Facial Expression Recognition to Interpersonal Relation Prediction
TL;DR: This work designs an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data and uses the expression recognition network as branches for a Siamese model to predict inter-personal relation.
325
References
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
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Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
- 07 Jul 2001
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Detecting faces in images: a survey
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
TL;DR: In this paper, the authors discuss human emotion perception from a psychological perspective, examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data.
2.5K