A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral

Pakize Erdoğmuş; Abdullah Talha Kabakuş; Enver Küçükkülahlı; Büşra Takgil; Ezgi Kara Timuçin

doi:10.35377/saucis...1337649

Araştırma Makalesi

Yıl 2023, Cilt: 6 Sayı: 3, 172 - 188, 31.12.2023

Pakize Erdoğmuş , Abdullah Talha Kabakuş , Enver Küçükkülahlı , Büşra Takgil , Ezgi Kara Timuçin

https://doi.org/10.35377/saucis...1337649

Öz

Kaynakça

[1] J. Wayman, A. Jain, D. Maltoni, and D. Maio, Biometric Systems, 1st ed. London: Springer London, 2005. doi: 10.1007/b138151.
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[5] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Local Descriptors to Improve Off-line Handwriting-based Gender Prediction,” in Proceedings of the 2014 6th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2014), Tunis, Tunisia: IEEE, pp. 43–47, 2014. doi: 10.1109/SOCPAR.2014.7007979.
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[10] R. S. Astur, A. J. Purton, M. J. Zaniewski, J. Cimadevilla, and E. J. Markus, “Human sex differences in solving a virtual navigation problem,” Behavioural Brain Research, vol. 308, pp. 236–243, 2016, doi: 10.1016/j.bbr.2016.04.037.
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[22] Á. Morera, Á. Sánchez, J. F. Vélez, and A. B. Moreno, “Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks,” Complexity, vol. 2018, pp. 1–14, 2018, doi: 10.1155/2018/3891624.
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[31] P. Maken and A. Gupta, “A method for automatic classification of gender based on text- independent handwriting,” Multimed Tools Appl, vol. 80, no. 16, pp. 24573–24602, Jul. 2021, doi: 10.1007/s11042-021-10837-9.
[32] G. Xue, S. Liu, D. Gong, and Y. Ma, “ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting,” Neural Comput Appl, vol. 33, pp. 4611–4622, May 2021, doi: 10.1007/s00521-020-05237-3.
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A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral

Yıl 2023, Cilt: 6 Sayı: 3, 172 - 188, 31.12.2023

Pakize Erdoğmuş , Abdullah Talha Kabakuş , Enver Küçükkülahlı , Büşra Takgil , Ezgi Kara Timuçin

https://doi.org/10.35377/saucis...1337649

Öz

Human handwriting is used to investigate human characteristics in various applications, including but not limited to biometric authentication, personality profiling, historical document analysis, and forensic investigations. Gender is one of the most distinguishing characteristics of human beings. From this point forth, we propose a novel end-to-end model based on Convolutional Neural Network (CNN) that automatically extracts features from a given handwritten sample, which contains both handwritten text and numerals unlike the related work that uses only handwritten text, and classifies its owner’s gender. In addition to proposing a novel model, we introduce a new dataset that consists of 530 gender-labeled Turkish handwritten samples since, to the best of our knowledge, there does not exist a public gender-labeled Turkish handwriting dataset. Following an exhaustive process of hyperparameter optimization, the proposed CNN featured the most optimal hyperparameters and was both trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 74.46%, which overperformed the state-of-the-art baselines and is promising on such a task that even humans could not have achieved highly-accurate results for, as of yet.

Anahtar Kelimeler

handwriting, gender classification, convolutional neural network, computer vision, forensic science

Kaynakça

[1] J. Wayman, A. Jain, D. Maltoni, and D. Maio, Biometric Systems, 1st ed. London: Springer London, 2005. doi: 10.1007/b138151.
[2] R. N. King and D. J. Koehler, “Illusory correlations in graphological inference,” J Exp Psychol Appl, vol. 6, no. 4, pp. 336–336, 2000, doi: 10.1037/1076-898X.6.4.336.
[3] V. Shackleton and S. Newell, “European Management Selection Methods: A Comparison of Five Countries,” International Journal of Selection and Assessment, vol. 2, no. 2, pp. 91–102, 1994, doi: 10.1111/j.1468-2389.1994.tb00155.x.
[4] M. Ahmed, A. G. Rasool, H. Afzal, and I. Siddiqi, “Improving handwriting based gender classification using ensemble classifiers,” Expert Syst Appl, vol. 85, pp. 158–168, 2017, doi: 10.1016/j.eswa.2017.05.033.
[5] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Local Descriptors to Improve Off-line Handwriting-based Gender Prediction,” in Proceedings of the 2014 6th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2014), Tunis, Tunisia: IEEE, pp. 43–47, 2014. doi: 10.1109/SOCPAR.2014.7007979.
[6] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Age, Gender and Handedness Prediction from Handwriting using Gradient Features,” in Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR ’15), Washington, DC, United States: IEEE, 2015, pp. 1116–1120. doi: 10.1109/ICDAR.2015.7333934.
[7] “What is gender? What is sex?,” Canadian Institutes of Health Research, 2020 [Online]. Available: https://cihr-irsc.gc.ca/e/48642.html. [Accessed: 10-Aug-2022].
[8] L. A. M. Galea and D. Kimura, “Sex differences in route-learning,” Pers Individ Dif, vol. 14, no. 1, pp. 53–65, 1993, doi: 10.1016/0191-8869(93)90174-2.
[9] E. Coluccia, G. Iosue, and M. Antonella Brandimonte, “The relationship between map drawing and spatial orientation abilities: A study of gender differences,” J Environ Psychol, vol. 27, no. 2, pp. 135–144, 2007, doi: 10.1016/j.jenvp.2006.12.005.
[10] R. S. Astur, A. J. Purton, M. J. Zaniewski, J. Cimadevilla, and E. J. Markus, “Human sex differences in solving a virtual navigation problem,” Behavioural Brain Research, vol. 308, pp. 236–243, 2016, doi: 10.1016/j.bbr.2016.04.037.
[11] D. A. Cahn-Weiner, K. Williams, J. Grace, G. Tremont, H. Westervelt, and R. A. Stern, “Discrimination of Dementia with Lewy bodies from Alzheimer disease and Parkinson disease using the Clock Drawing Test,” Cognitive and Behavioral Neurology, vol. 16, no. 2, pp. 85–92, 2003, doi: 10.1097/00146965-200306000-00001.
[12] K. Amunts et al., “Gender-specific left-right asymmetries in human visual cortex,” Journal of Neuroscience, vol. 27, no. 6, pp. 1356–1364, 2007, doi: 10.1523/JNEUROSCI.4753-06.2007.
[13] A. C. Hurlbert and Y. Ling, “Biological components of sex differences in color preference,” Current Biology, vol. 17, no. 16, pp. 623–625, 2007, doi: 10.1016/j.cub.2007.06.022.
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[15] G. Cordasco, M. Buonanno, M. Faundez-Zanuy, M. T. Riviello, L. Likforman-Sulem, and A. Esposito, “Gender Identification through Handwriting: an Online Approach,” in Proceedings of the 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2020), Mariehamn, Finland: IEEE, pp. 197–202, 2020. doi: 10.1109/CogInfoCom50765.2020.9237863.
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[22] Á. Morera, Á. Sánchez, J. F. Vélez, and A. B. Moreno, “Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks,” Complexity, vol. 2018, pp. 1–14, 2018, doi: 10.1155/2018/3891624.
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[25] S. al Maadeed and A. Hassaine, “Automatic prediction of age, gender, and nationality in offline handwriting,” EURASIP J Image Video Process, vol. 2014, no. 10, pp. 1–10, 2014, doi: 10.1186/1687-5281-2014-10.
[26] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Histogram of Oriented Gradients for Writer’s Gender, Handedness and Age prediction,” in Proceedings of the 2015 International Symposium on Innovations in Intelligent SysTems and Applications, Proceedings (INISTA 2015), pp. 1–5, 2015. doi: 10.1109/INISTA.2015.7276752.
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[28] Y. Akbari, K. Nouri, J. Sadri, C. Djeddi, and I. Siddiqi, “Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata,” Image Vis Comput, vol. 59, no. C, pp. 17–30, 2017, doi: 10.1016/j.imavis.2016.11.017.
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[30] A. E. Youssef, A. S. Ibrahim, and A. Lynn Abbott, “Automated Gender Identification for Arabic and English Handwriting,” in Proceedings of the 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013), London, UK, pp. 1–6. Doi, 2013: 10.1049/ic.2013.0274.
[31] P. Maken and A. Gupta, “A method for automatic classification of gender based on text- independent handwriting,” Multimed Tools Appl, vol. 80, no. 16, pp. 24573–24602, Jul. 2021, doi: 10.1007/s11042-021-10837-9.
[32] G. Xue, S. Liu, D. Gong, and Y. Ma, “ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting,” Neural Comput Appl, vol. 33, pp. 4611–4622, May 2021, doi: 10.1007/s00521-020-05237-3.
[33] E. Belval, “pdf2image: A python (3.6+) module that wraps pdftoppm and pdftocairo to convert PDF to a PIL Image object,” 2021. [Online]. Available: https://github.com/Belval/pdf2image [Accessed: 10-Aug-2022].
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Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Bilgisayar Yazılımı
Bölüm	Makaleler
Yazarlar	Pakize Erdoğmuş 0000-0003-2172-5767 Abdullah Talha Kabakuş 0000-0003-2181-4292 Enver Küçükkülahlı 0000-0002-0525-0477 Büşra Takgil 0000-0002-7927-0083 Ezgi Kara Timuçin 0000-0003-1596-3427
Erken Görünüm Tarihi	27 Aralık 2023
Yayımlanma Tarihi	31 Aralık 2023
Gönderilme Tarihi	4 Ağustos 2023
Kabul Tarihi	28 Eylül 2023
Yayımlandığı Sayı	Yıl 2023Cilt: 6 Sayı: 3

Kaynak Göster

IEEE	P. Erdoğmuş, A. T. Kabakuş, E. Küçükkülahlı, B. Takgil, ve E. Kara Timuçin, “A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral”, SAUCIS, c. 6, sy. 3, ss. 172–188, 2023, doi: 10.35377/saucis...1337649.

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