Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces

• URL: http://arxiv.org/abs/2311.11980v1
• Date: Mon, 20 Nov 2023 18:14:53 GMT
• Title: Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces
• Authors: Pietro B. S. Masur and Willams Costa and Lucas S. Figueredo and Veronica Teichrieb
• Abstract summary: We propose the usage of Facial Action Units (AUs) recognition techniques to recognize emotions. This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system.
• Score: 2.4158349218144393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People naturally understand emotions, thus permitting a machine to do the same could open new paths for human-computer interaction. Facial expressions can be very useful for emotion recognition techniques, as these are the biggest transmitters of non-verbal cues capable of being correlated with emotions. Several techniques are based on Convolutional Neural Networks (CNNs) to extract information in a machine learning process. However, simple CNNs are not always sufficient to locate points of interest on the face that can be correlated with emotions. In this work, we intend to expand the capacity of emotion recognition techniques by proposing the usage of Facial Action Units (AUs) recognition techniques to recognize emotions. This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system. In particular, our method expands over EmotiRAM, an approach for multi-cue emotion recognition, in which we improve over their facial encoding module.

Related papers

• Emotion Detection through Body Gesture and Face [0.0]
  The project addresses the challenge of emotion recognition by focusing on non-facial cues, specifically hands, body gestures, and gestures. Traditional emotion recognition systems mainly rely on facial expression analysis and often ignore the rich emotional information conveyed through body language. The project aims to contribute to the field of affective computing by enhancing the ability of machines to interpret and respond to human emotions in a more comprehensive and nuanced way.
  arXiv  Detail & Related papers  (2024-07-13T15:15:50Z)
• Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News [21.707761612280304]
  We present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resource-intensive, whereas our approach with Facial Landmark Emotion Recognition (FLER) offers a simplified yet effective alternative.
  arXiv  Detail & Related papers  (2024-04-21T00:14:03Z)
• Multi-Cue Adaptive Emotion Recognition Network [4.570705738465714]
  We propose a new deep learning approach for emotion recognition based on adaptive multi-cues. We compare the proposed approach with the state-of-art approaches in the CAER-S dataset.
  arXiv  Detail & Related papers  (2021-11-03T15:08:55Z)
• Stimuli-Aware Visual Emotion Analysis [75.68305830514007]
  We propose a stimuli-aware visual emotion analysis (VEA) method consisting of three stages, namely stimuli selection, feature extraction and emotion prediction. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets.
  arXiv  Detail & Related papers  (2021-09-04T08:14:52Z)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)
• Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey [8.31246680772592]
  We present recent research in the field of using neural networks to recognize emotions. We focus on studying emotions' recognition from speech, facial expressions, and audio-visual input. These three emotion recognition techniques can be used as a surveillance system in healthcare centers to monitor patients.
  arXiv  Detail & Related papers  (2021-07-13T11:17:00Z)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)
• Learning Emotional-Blinded Face Representations [77.7653702071127]
  We propose two face representations that are blind to facial expressions associated to emotional responses. This work is motivated by new international regulations for personal data protection.
  arXiv  Detail & Related papers  (2020-09-18T09:24:10Z)
• Emotion Recognition From Gait Analyses: Current Research and Future Directions [48.93172413752614]
  gait conveys information about the walker's emotion. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject.
  arXiv  Detail & Related papers  (2020-03-13T08:22:33Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)
• An adversarial learning framework for preserving users' anonymity in face-based emotion recognition [6.9581841997309475]
  This paper proposes an adversarial learning framework which relies on a convolutional neural network (CNN) architecture trained through an iterative procedure. Results indicate that the proposed approach can learn a convolutional transformation for preserving emotion recognition accuracy and degrading face identity recognition.
  arXiv  Detail & Related papers  (2020-01-16T22:45:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.