SAFER: Situation Aware Facial Emotion Recognition

• URL: http://arxiv.org/abs/2306.09372v1
• Date: Wed, 14 Jun 2023 20:42:26 GMT
• Title: SAFER: Situation Aware Facial Emotion Recognition
• Authors: Mijanur Palash, Bharat Bhargava
• Abstract summary: We present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features from facial images. It can adapt to unseen and varied facial expressions, making it suitable for real-world applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features from facial images and incorporates contextual information, such as background and location type, to enhance its performance. The system has been designed to operate in an open-world setting, meaning it can adapt to unseen and varied facial expressions, making it suitable for real-world applications. An extensive evaluation of SAFER against existing works in the field demonstrates improved performance, achieving an accuracy of 91.4% on the CAER-S dataset. Additionally, the study investigates the effect of novelty such as face masks during the Covid-19 pandemic on facial emotion recognition and critically examines the limitations of mainstream facial expressions datasets. To address these limitations, a novel dataset for facial emotion recognition is proposed. The proposed dataset and the system are expected to be useful for various applications such as human-computer interaction, security, and surveillance.

Related papers

• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers [57.1091606948826]
  We propose a novel FER model, named Poker Face Vision Transformer or PF-ViT, to address these challenges. PF-ViT aims to separate and recognize the disturbance-agnostic emotion from a static facial image via generating its corresponding poker face. PF-ViT utilizes vanilla Vision Transformers, and its components are pre-trained as Masked Autoencoders on a large facial expression dataset.
  arXiv  Detail & Related papers  (2022-07-22T13:39:06Z)
• I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition [78.07239208222599]
  We evaluate how the recently proposed FaceChannel adapts towards recognizing facial expressions from persons with masks. We also perform specific feature-level visualization to demonstrate how the inherent capabilities of the FaceChannel to learn and combine facial features change when in a constrained social interaction scenario.
  arXiv  Detail & Related papers  (2021-04-16T20:03:30Z)
• A Multi-resolution Approach to Expression Recognition in the Wild [9.118706387430883]
  We propose a multi-resolution approach to solve the Facial Expression Recognition task. We ground our intuition on the observation that often faces images are acquired at different resolutions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset.
  arXiv  Detail & Related papers  (2021-03-09T21:21:02Z)
• Facial Expressions as a Vulnerability in Face Recognition [73.85525896663371]
  This work explores facial expression bias as a security vulnerability of face recognition systems. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies.
  arXiv  Detail & Related papers  (2020-11-17T18:12:41Z)
• Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions [11.808447247077902]
  This paper proposes a feature-based domain adaptation technique for identifying emotions in generic images. It addresses the challenge of the limited availability of pre-trained models and well-annotated datasets for Image Emotion Recognition (IER) The proposed IER system demonstrated emotion classification accuracies of 60.98% for the IAPSa dataset, 58.86% for the ArtPhoto dataset, 69.13% for the FI dataset, and 58.06% for the EMOTIC dataset.
  arXiv  Detail & Related papers  (2020-11-17T02:55:16Z)
• 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)
• Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity [6.974241731162878]
  This paper proposes a novel method for human emotion recognition from a single RGB image. We construct a large-scale dataset of facial videos, rich in facial dynamics, identities, expressions, appearance and 3D pose variations. Our proposed framework runs at 50 frames per second and is capable of robustly estimating parameters of 3D expression variation.
  arXiv  Detail & Related papers  (2020-05-12T01:32:55Z)
• Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition [98.83578105374535]
  We present a novel Fine-grained Facial Expression Database - F2ED. It includes more than 200k images with 54 facial expressions from 119 persons. Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we evaluate several tasks of few-shot expression learning. We propose a unified task-driven framework - Compositional Generative Adversarial Network (Comp-GAN) learning to synthesize facial images.
  arXiv  Detail & Related papers  (2020-01-17T03:26:32Z)
• 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.