International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 14, No. 5, 8 Oct. 2024
Cover page and Table of Contents: PDF (size: 1023KB)
Gender Classification Optimization with Thermal Images Using Advanced Neural Networks
PDF (1023KB), PP.37-51
Views: 0 Downloads: 0
Author(s)
Kethineni Keerthi
1
Gurram Harika
1,*
Kommineni Deva Harshini
1
Kakani Soumya
1
Index Terms
Gender classification, Convolutional Neural networks, Thermal images, Deep learning, AlexNet, Inception V3
Abstract
In this study, we investigate the effectiveness of deep learning models with thermal images for gender categorization. In order to explore the possibilities of thermal imaging as a tool for gender identification, the study focuses on two sophisticated convolutional neural network (CNN) architectures: InceptionV3 and AlexNet. Thermal imaging is a powerful substitute for traditional visual data because it provides distinct physiological insights.A collection of thermal imaging datasets was assembled, methodically preprocessed, and divided into training and testing sets. For this comparison analysis, two well-known CNNs AlexNet, a fundamental model recognised for its straightforward yet efficient design, and InceptionV3, a complex model acclaimed for its inception modules were chosen. The training subset was used to carefully refine both models so they could accurately capture the subtleties of thermal-based gender traits.Accuracy was the main criterion used to assess the performance of the revised models on the testing subset. According to our results, InceptionV3 performs noticeably better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This disparity in performance demonstrates how much better InceptionV3 is at identifying and deciphering minute thermal patterns and physiological indicators that are essential for precise gender categorization. This study highlights how sophisticated CNN architectures may improve gender categorization using thermal images, both in terms of accuracy and dependability. We provide a path for future research to investigate more intricate and integrated strategies, like multi-modal fusion and sophisticated feature extraction techniques, to further enhance the resilience of thermal-based gender classification systems by proving the efficacy of InceptionV3 over a more conventional model like AlexNet.
Cite This Paper
Kethineni Keerthi, Gurram Harika, Kommineni Deva Harshini, Kakani Soumya, "Gender Classification Optimization with Thermal Images Using Advanced Neural Networks", International Journal of Engineering and Manufacturing (IJEM), Vol.14, No.5, pp. 37-51, 2024. DOI:10.5815/ijem.2024.05.04
Reference
[1]Sayed, Mohamed, and Faris Baker. "Thermal face authentication with convolutional neural network." J. Comput. Sci 14.12 (2018): 1627-1637.
[2]Verschae, Rodrigo, Javier Ruiz-del-Solar, and Mauricio Correa. "Gender classification of faces using adaboost." Progress in Pattern Recognition, Image Analysis and Applications: 11th Iberoamerican Congress in Pattern Recognition, CIARP 2006 Cancun, Mexico, November 14-17, 2006 Proceedings 11. Springer Berlin Heidelberg, 2006.
[3]Nguyen, Dat Tien, et al. "Gender recognition from human-body images using visible-light and thermal camera videos based on a convolutional neural network for image feature extraction." Sensors 17.3 (2017): 637.
[4]Zhang, Baochang, et al. "Directional binary code with application to PolyU near-infrared face database." Pattern recognition letters 31.14 (2010): 2337-2344.
[5]Bourlai, T., et al. "Ascertaining human identity in night environments." Distributed Video Sensor Networks (2011): 451-467.
[6]Al_Dujaili, Mohammed Jawad, Haider TH Salim ALRikabi, and Ibtihal Razaq Niama ALRubeei. "Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers." International Journal of Interactive Mobile Technologies 17.8 (2023).
[7]Jalil, Alyaa J., and Naglaa M. Reda. "Infrared thermal image gender classifier based on the deep Resnet model." Advances in Human-Computer Interaction 2022 (2022).
[8]Baek, Na Rae, et al. "Pedestrian gender recognition by style transfer of visible-light image to infrared-light image based on an attention-guided generative adversarial network." Mathematics 9.20 (2021): 2535.
[9]Gwyn, Tony, and Kaushik Roy. "Examining Gender Bias of Convolutional Neural Networks via Facial Recognition." Future Internet 14.12 (2022): 375.
[10]Kowalski, Marcin, Artur Grudzień, and Krzysztof Mierzejewski. "Thermal–visible face recognition based on CNN features and triple triplet configuration for on-the-move identity verification." Sensors 22.13 (2022): 5012.
[11]Cao, Zhicheng, et al. "Infrared-Based Gender Recognition from Facial Images: A Comparative Study Using Deep Learning."
[12]Müller, David, Andreas Ehlen, and Bernd Valeske. "Convolutional neural networks for semantic segmentation as a tool for multiclass face analysis in thermal infrared." Journal of nondestructive evaluation 40.1 (2021): 9.
[13]Jalil, Alyaa Jaber, et al. "Modified CNN Model for Classifying Gender of Thermal Images Using Cloud Computing." Informatica 47.10 (2024).
[14]Khan, Khalil, et al. "Automatic gender classification through face segmentation." Symmetry 11.6 (2019): 770.
[15]Sheikh Fathollahi, Mohammadreza, and Rezvan Heidari. "Gender classification from face images using central difference convolutional networks." International Journal of Multimedia Information Retrieval 11.4 (2022): 695-703.
[16]Waris, Fazal, Feipeng Da, and Shanghuan Liu. "Deep learning based features extraction for facial gender classification using ensemble of machine learning technique." Multimedia Systems 30.4 (2024): 1-21.
[17]Qawaqneh, Zakariya, Arafat Abu Mallouh, and Buket D. Barkana. "Age and gender classification from speech and face images by jointly fine-tuned deep neural networks." Expert Systems with Applications 85 (2017): 76-86.
[18]Das, Abhijit, Antitza Dantcheva, and Francois Bremond. "Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach." Proceedings of the european conference on computer vision (eccv) workshops. 2018.
[19]Wang, Shangfei, et al. "Gender recognition from visible and thermal infrared facial images." Multimedia Tools and Applications 75 (2016): 8419-8442.
[20]Tilki, Sahra, Hasibe Busra Dogru, and Alaa Ali Hameed. "Gender classification using deep learning techniques." Manchester journal of Artificial Intelligence and Applied sciences 2.2 (2021).
[21]Manyala, Anirudh, et al. "CNN-based gender classification in near-infrared periocular images." Pattern Analysis and Applications 22 (2019): 1493-1504.
[22]Danisman, Taner, Ioan Marius Bilasco, and Jean Martinet. "Boosting gender recognition performance with a fuzzy inference system." Expert Systems with Applications 42.5 (2015): 2772-2784.
[23]Nguyen, Dat Tien, and Kang Ryoung Park. "Enhanced gender recognition system using an improved histogram of oriented gradient (HOG) feature from quality assessment of visible light and thermal images of the human body." Sensors 16.7 (2016): 1134.
[24]Fekri-Ershad, Shervan. "Gender classification in human face images for smart phone applications based on local texture information and evaluated Kullback-Leibler divergence." Traitement du Signal 36.6 (2019): 507-514.
[25]Nair, Revathi Ramachandran, Reshma Madhavankutty, and Shikha Nema. "Automated detection of gender from face images." International Research Journal of Engineering and Technology 6 (2019).