Have a personal or library account? Click to login
Gender determination from periocular images using deep learning based EfficientNet architecture Cover

Gender determination from periocular images using deep learning based EfficientNet architecture

Open Access
|Oct 2023

Figures & Tables

Fig. 1

Periocular region.
Periocular region.

Fig. 2

CNN architecture.
CNN architecture.

Fig. 3

Stem layer for EfficientNet-B1 [19].
Stem layer for EfficientNet-B1 [19].

Fig. 4

Architecture for EfficientNet-B1 [19].
Architecture for EfficientNet-B1 [19].

Fig. 5

Modules for EfficientNet-B1 [19].
Modules for EfficientNet-B1 [19].

Fig. 6

A subset of the dataset.
A subset of the dataset.

Fig. 7

Confusion matrix for CNN model built from scratch.
Confusion matrix for CNN model built from scratch.

Fig. 8

Model accuracy for CNN built from scratch.
Model accuracy for CNN built from scratch.

Fig. 9

Model loss for CNN built from scratch.
Model loss for CNN built from scratch.

Fig. 10

Confusion matrix for CNN model built from scratch.
Confusion matrix for CNN model built from scratch.

Fig. 11

Model accuracy for EfficientNetB1.
Model accuracy for EfficientNetB1.

Fig. 12

Model loss for EfficientNetB1.
Model loss for EfficientNetB1.

Classification report for fine-tuned EfficientNetB1_

LabelPrecisionRecallF1-score

1 (Male)0.970.990.98
0 (Female)0.990.970.98

Classification report for CNN model built from scratch_

LabelPrecisionRecallF1-score

1 (Male)0.940.960.95
0 (Female)0.950.930.94
Language: English
Page range: 59 - 70
Submitted on: Jul 22, 2023
Accepted on: Sep 8, 2023
Published on: Oct 31, 2023
Published by: Harran University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 Viji B Nambiar, Bojan Ramamurthy, Pundikala Veeresha, published by Harran University
This work is licensed under the Creative Commons Attribution 4.0 License.