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New Challenges of Face Detection Based on Deep Learning
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New Challenges of Face Detection Based on Deep Learning

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 5851

Special Issue Editors

Dr. André Bigand

E-Mail Website
Guest Editor
ULCO, LISIC, 62228 Calais, France
Interests: image processing; deep learning; face detection; paintings
Prof. Dr. Thierry Bouwmans

E-Mail Website
Guest Editor
MIA-Lab, Université La Rochelle, 17042 La Rochelle, France
Interests: background modeling; face detection; deep learning; graph signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, a lot of attention has been devoted to the successful development of facial recognition, which is often preceded by the classical techniques of face detection. However, face detection has also recently become an important topic, and this is particularly due to the development of new machine learning methods. These recent methods make it possible to tackle new challenges in image processing (face detection in a crowd, face tracking, painting analysis, etc.) with high performance. Thus, the aim of this Special Issue is to collect leading recent advances in face detection using practicable algorithms based on CNNs, GANs, etc.

Dr. André Bigand
Prof. Dr. Thierry Bouwmans
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • face detection
  • paintings
  • tracking
  • explainability
  • interpretability

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Published Papers (1 paper)

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Research

17 pages, 603 KiB  
Article
Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations
by Sirine Ammar, Thierry Bouwmans and Mahmoud Neji
Information 2022, 13(8), 370; https://doi.org/10.3390/info13080370 - 3 Aug 2022
Cited by 8 | Viewed by 4807
Abstract
Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vision for a broad range of applications, including image classification and face recognition. Compared to existing conventional machine learning methods, deep learning algorithms have shown prominent performance with high [...] Read more.
Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vision for a broad range of applications, including image classification and face recognition. Compared to existing conventional machine learning methods, deep learning algorithms have shown prominent performance with high accuracy and speed. However, they always require a large amount of data to achieve adequate robustness. Furthermore, additional samples are time-consuming and expensive to collect. In this paper, we propose an approach that combines generative methods and basic manipulations for image data augmentations and the FaceNet model with Support Vector Machine (SVM) for face recognition. To do so, the images were first preprocessed by a Deep Convolutional Generative Adversarial Net (DCGAN) to generate samples having realistic properties inseparable from those of the original datasets. Second, basic manipulations were applied on the images produced by DCGAN in order to increase the amount of training data. Finally, FaceNet was employed as a face recognition model. FaceNet detects faces using MTCNN, 128-D face embedding is computed to quantify each face, and an SVM was used on top of the embeddings for classification. Experiments carried out on the LFW and VGG image databases and ChokePoint video database demonstrate that the combination of basic and generative methods for augmentation boosted face recognition performance, leading to better recognition results. Full article
(This article belongs to the Special Issue New Challenges of Face Detection Based on Deep Learning)
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