<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this study, we obtain a sex prediction algorithm based on CNN in two ways - building a red Convolutional Neural Network (CNN) model from scratch and via transfer learning. We built a model from scratch and compared it with fine-tuned EfficientNetB1. We use these models for gender determination using periocular images and compare the two models depending on the accuracy of the models. The CNN model proposed from scratch yields an accuracy of 94.46% while the fine-tuned EfficientNetB1 yields an accuracy of 97.94%. This paper is one of the first works in determining gender from periocular images in the visible spectrum using a CNN model built from the outset.</p>
</abstract>

In this study, we obtain a sex prediction algorithm based on CNN in two ways - building a red Convolutional Neural Network (CNN) model from scratch and via transfer learning. We built a model from scratch and compared it with fine-tuned EfficientNetB1. We use these models for gender determination using periocular images and compare the two models depending on the accuracy of the models. The CNN model proposed from scratch yields an accuracy of 94.46% while the fine-tuned EfficientNetB1 yields an accuracy of 97.94%. This paper is one of the first works in determining gender from periocular images in the visible spectrum using a CNN model built from the outset.

Gender determination from periocular images using deep learning based EfficientNet architecture

International Journal of Mathematics and Computer in Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this study, we obtain a sex prediction algorithm based on CNN in two ways - building a red Convolutional Neural Network (CNN) model from scratch and via...

Label	Precision	Recall	F1-score

1 (Male)	0.97	0.99	0.98
0 (Female)	0.99	0.97	0.98

Label	Precision	Recall	F1-score

1 (Male)	0.94	0.96	0.95
0 (Female)	0.95	0.93	0.94

Gender determination from periocular images using deep learning based EfficientNet architecture

Article Category: Original Study

Data publikacji: 31 paź 2023

Zakres stron: 59 - 70

Otrzymano: 22 lip 2023

Przyjęty: 08 wrz 2023

DOI: https://doi.org/10.2478/ijmce-2024-0005

Słowa kluczowe
CNN, EfficientNetB1, periocular, fine-tuning

© 2024 Viji B Nambiar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Classification report for fine-tuned EfficientNetB1.

Classification report for CNN model built from scratch.

Gender determination from periocular images using deep learning based EfficientNet architecture

Article Category: Original Study

Data publikacji: 31 paź 2023

Zakres stron: 59 - 70

Otrzymano: 22 lip 2023

Przyjęty: 08 wrz 2023

DOI: https://doi.org/10.2478/ijmce-2024-0005

Słowa kluczoweCNN, EfficientNetB1, periocular, fine-tuning

© 2024 Viji B Nambiar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

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Fig. 5

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Fig. 9

Fig. 10

Fig. 11

Fig. 12

Classification report for fine-tuned EfficientNetB1.

Classification report for CNN model built from scratch.

Słowa kluczowe
CNN, EfficientNetB1, periocular, fine-tuning