Academic Journal

Using Diverse ConvNets to Classify Face Action Units in Dataset on Emotions Among Mexicans (DEM)

التفاصيل البيبلوغرافية
العنوان: Using Diverse ConvNets to Classify Face Action Units in Dataset on Emotions Among Mexicans (DEM)
المؤلفون: Marco A. Moreno-Armendariz, Alberto Espinosa-Juarez, Esmeralda Godinez-Montero
المصدر: IEEE Access, Vol 12, Pp 15268-15279 (2024)
بيانات النشر: IEEE, 2024.
سنة النشر: 2024
المجموعة: LCC:Electrical engineering. Electronics. Nuclear engineering
مصطلحات موضوعية: Action units, ConvNets, CAM analysis, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
الوصف: To understand how Convolutional Neural Networks (ConvNets) perceive the muscular movements of the human face, known as Action Units (AUs) in this work, we introduce a new dataset named Dataset on Emotions among Mexicans (DEM), consisting of 1557 images of Mexicans labeled with twenty-six AUs and seven emotions. As a benchmark, we used the comparison with DISFA+ labeled with 12 AUs. To address the task of detecting AUs in each image, six ConvNets were employed, and we evaluated their performance using the F1 Score. The two ConvNets with the best performance were VGG19 with 0.8180% (DEM), 0.9106 % (DISFA+), and ShuffleNetV2 with 0.7154% (DEM), 0.9440% (DISFA+). Subsequently, these ConvNets were analyzed using Grad-CAM and Grad-CAM++; this algorithms allows us to observe the areas of the face considered for prediction. In most cases, these areas consider the region of the labeled AU. Considering the F1 score and the visual study, we can conclude that using DEM as a dataset to classify AUs is promising since the experiments achieved performances similar to those of the current literature that only use ConvNets.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2169-3536
Relation: https://ieeexplore.ieee.org/document/10413357/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2024.3358207
URL الوصول: https://doaj.org/article/7a2cb656673e40a091875703300c37e6
رقم الانضمام: edsdoj.7a2cb656673e40a091875703300c37e6
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21693536
DOI:10.1109/ACCESS.2024.3358207