Spontaneous Emotion Recognition from Facial Thermal Images

• URL: http://arxiv.org/abs/2012.06973v1
• Date: Sun, 13 Dec 2020 05:55:19 GMT
• Title: Spontaneous Emotion Recognition from Facial Thermal Images
• Authors: Chirag Kyal
• Abstract summary: We analyze that a large number of tasks for facial image processing in thermal infrared images can be addressed with modern learning-based approaches. We have used USTC-NVIE database for training of a number of machine learning algorithms for facial landmark localization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: One of the key research areas in computer vision addressed by a vast number of publications is the processing and understanding of images containing human faces. The most often addressed tasks include face detection, facial landmark localization, face recognition and facial expression analysis. Other, more specialized tasks such as affective computing, the extraction of vital signs from videos or analysis of social interaction usually require one or several of the aforementioned tasks that have to be performed. In our work, we analyze that a large number of tasks for facial image processing in thermal infrared images that are currently solved using specialized rule-based methods or not solved at all can be addressed with modern learning-based approaches. We have used USTC-NVIE database for training of a number of machine learning algorithms for facial landmark localization.

Related papers

• Task-adaptive Q-Face [75.15668556061772]
  We propose a novel task-adaptive multi-task face analysis method named as Q-Face. Q-Face simultaneously performs multiple face analysis tasks with a unified model. Our method achieves state-of-the-art performance on face expression recognition, action unit detection, face attribute analysis, age estimation, and face pose estimation.
  arXiv  Detail & Related papers  (2024-05-15T03:13:11Z)
• LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition [37.4550614524874]
  We focus on learning facial representations that can be adapted to train effective face recognition models. We explore the learning strategy of unlabeled facial images through self-supervised pretraining. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks.
  arXiv  Detail & Related papers  (2024-03-13T01:07:55Z)
• Self-Supervised Facial Representation Learning with Facial Region Awareness [13.06996608324306]
  Self-supervised pre-training has been proven to be effective in learning transferable representations that benefit various visual tasks. Recent efforts toward this goal are limited to treating each face image as a whole. We propose a novel self-supervised facial representation learning framework to learn consistent global and local facial representations.
  arXiv  Detail & Related papers  (2024-03-04T15:48:56Z)
• A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios [0.0]
  This study presents comparative benchmark tables for the state-of-art face recognition methods. We constructed a video surveillance dataset of face IDs with high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.
  arXiv  Detail & Related papers  (2022-11-05T17:59:18Z)
• 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)
• Facial Action Unit Recognition Based on Transfer Learning [22.34261589991243]
  We introduce a facial action unit recognition method based on transfer learning. We first use available facial images with expression labels to train the feature extraction network.
  arXiv  Detail & Related papers  (2022-03-25T04:01:58Z)
• Psychophysical Evaluation of Human Performance in Detecting Digital Face Image Manipulations [14.63266615325105]
  This work introduces a web-based, remote visual discrimination experiment on the basis of principles adopted from the field of psychophysics. We examine human proficiency in detecting different types of digitally manipulated face images, specifically face swapping, morphing, and retouching.
  arXiv  Detail & Related papers  (2022-01-28T12:45:33Z)
• Pre-training strategies and datasets for facial representation learning [58.8289362536262]
  We show how to find a universal face representation that can be adapted to several facial analysis tasks and datasets. We systematically investigate two ways of large-scale representation learning applied to faces: supervised and unsupervised pre-training. Our main two findings are: Unsupervised pre-training on completely in-the-wild, uncurated data provides consistent and, in some cases, significant accuracy improvements.
  arXiv  Detail & Related papers  (2021-03-30T17:57:25Z)
• 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)
• From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback [72.29919762941029]
  We propose an end-to-end framework to retrieve facial images with relevance feedback progressively provided by the witness. With no need of any extra annotations, our model can be applied at the cost of a little response effort.
  arXiv  Detail & Related papers  (2020-07-30T18:46:25Z)
• 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)

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.