Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique

• URL: http://arxiv.org/abs/2312.01335v1
• Date: Sun, 3 Dec 2023 09:50:46 GMT
• Title: Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
• Authors: Aref Farhadipour, Pouya Taghipour
• Abstract summary: We propose a facial emotion recognition system capable of recognizing emotions from individuals wearing different face masks. We evaluated the effectiveness of four convolutional neural networks that were trained using transfer learning. The Resnet50 has demonstrated superior performance, with accuracies of 73.68% for the person-dependent mode and 59.57% for the person-independent mode.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying human emotions using AI-based computer vision systems, when individuals wear face masks, presents a new challenge in the current Covid-19 pandemic. In this study, we propose a facial emotion recognition system capable of recognizing emotions from individuals wearing different face masks. A novel data augmentation technique was utilized to improve the performance of our model using four mask types for each face image. We evaluated the effectiveness of four convolutional neural networks, Alexnet, Squeezenet, Resnet50 and VGGFace2 that were trained using transfer learning. The experimental findings revealed that our model works effectively in multi-mask mode compared to single-mask mode. The VGGFace2 network achieved the highest accuracy rate, with 97.82% for the person-dependent mode and 74.21% for the person-independent mode using the JAFFE dataset. However, we evaluated our proposed model using the UIBVFED dataset. The Resnet50 has demonstrated superior performance, with accuracies of 73.68% for the person-dependent mode and 59.57% for the person-independent mode. Moreover, we employed metrics such as precision, sensitivity, specificity, AUC, F1 score, and confusion matrix to measure our system's efficiency in detail. Additionally, the LIME algorithm was used to visualize CNN's decision-making strategy.

Related papers

• Masked Face Dataset Generation and Masked Face Recognition [2.4377103193902703]
  In post-pandemic era, wearing face masks has posed great challenge to the ordinary face recognition. To make the model more adaptable to the real world situation, we created a more challenging masked face dataset. The best test accuracy on 50 identity MFR has achieved 95%.
  arXiv  Detail & Related papers  (2023-11-13T17:09:57Z)
• One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations [59.17685450892182]
  We investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images. Traditional algorithms like SIFT can outperform CNNs in situations with limited data.
  arXiv  Detail & Related papers  (2023-07-11T09:10:16Z)
• Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition [1.0742675209112622]
  Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models.
  arXiv  Detail & Related papers  (2022-10-10T16:25:13Z)
• A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19 [0.0]
  WHO recommends wearing face masks as one of the most effective measures to prevent COVID-19 transmission. We explore a number of deep learning models for face-mask detection and evaluate them on two benchmark datasets. We find that while the performances of all the models are quite good, transfer learning models achieve the best performance.
  arXiv  Detail & Related papers  (2021-12-15T06:30:50Z)
• Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models [66.07662074148142]
  We propose a physical adversarial universal perturbation (UAP) against state-of-the-art deep learning-based facial recognition models. In our experiments, we examined the transferability of our adversarial mask to a wide range of deep learning models and datasets. We validated our adversarial mask effectiveness in real-world experiments by printing the adversarial pattern on a fabric medical face mask.
  arXiv  Detail & Related papers  (2021-11-21T08:13:21Z)
• Facial Emotion Recognition: State of the Art Performance on FER2013 [0.0]
  We achieve the highest single-network classification accuracy on the FER2013 dataset. Our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.
  arXiv  Detail & Related papers  (2021-05-08T04:20:53Z)
• Boosting Masked Face Recognition with Multi-Task ArcFace [0.973681576519524]
  Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. A full training pipeline is presented based on the ArcFace work, with several modifications for the backbone and the loss function.
  arXiv  Detail & Related papers  (2021-04-20T10:12:04Z)
• 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)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)
• A Computer Vision System to Help Prevent the Transmission of COVID-19 [79.62140902232628]
  The COVID-19 pandemic affects every area of daily life globally. Health organizations advise social distancing, wearing face mask, and avoiding touching face. We developed a deep learning-based computer vision system to help prevent the transmission of COVID-19.
  arXiv  Detail & Related papers  (2021-03-16T00:00:04Z)
• The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition [71.24825724518847]
  Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
  arXiv  Detail & Related papers  (2020-09-15T09:25:37Z)

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.