Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations
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 perfo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/8/370 |
| _version_ | 1797409690982809600 |
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| author | Sirine Ammar Thierry Bouwmans Mahmoud Neji |
| author_facet | Sirine Ammar Thierry Bouwmans Mahmoud Neji |
| author_sort | Sirine Ammar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:18:13Z |
| format | Article |
| id | doaj.art-a8007e7f2b3840259bcf2d7fc9b7a64d |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T04:18:13Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-a8007e7f2b3840259bcf2d7fc9b7a64d2023-12-03T13:50:49ZengMDPI AGInformation2078-24892022-08-0113837010.3390/info13080370Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic ManipulationsSirine Ammar0Thierry Bouwmans1Mahmoud Neji2Laboratoire MIA, Université de La Rochelle, Avenue M. Crépeau, 17000 La Rochelle, FranceLaboratoire MIA, Université de La Rochelle, Avenue M. Crépeau, 17000 La Rochelle, FranceLaboratoire MIRACL, Université de Sfax, Route de l’Aéroport, Sfax 3029, TunisiaRecently, 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.https://www.mdpi.com/2078-2489/13/8/370generative methodsbasic manipulationsdata augmentationFaceNetSVMface recognition |
| spellingShingle | Sirine Ammar Thierry Bouwmans Mahmoud Neji Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations Information generative methods basic manipulations data augmentation FaceNet SVM face recognition |
| title | Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations |
| title_full | Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations |
| title_fullStr | Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations |
| title_full_unstemmed | Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations |
| title_short | Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations |
| title_sort | face identification using data augmentation based on the combination of dcgans and basic manipulations |
| topic | generative methods basic manipulations data augmentation FaceNet SVM face recognition |
| url | https://www.mdpi.com/2078-2489/13/8/370 |
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