Research Article
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Year 2020, Issue: 045, 111 - 125, 31.12.2020

Abstract

References

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  • [2] Bishop, C. M., (2006) Pattern Recognition and Machine Learning. Springer Science + Business Media.
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  • [8] Jialin Pan, S., and Yang, Q., (2010) “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng., vol. 22, pp. 1345–1359.
  • [9] Koçer, B., (2012) “Transfer Öğrenmede Yeni Yaklaşımlar,” Selçuk University.
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  • [11] Baek, N. R., Cho, S. W., Koo, J. H., Truong, N. Q. and Park, K. R.,(2019), “Multimodal Camera-Based Gender Recognition Using Human-Body Image With Two-Step Reconstruction Network,” IEEE Access, vol. 7, pp. 104025–104044, doi: 10.1109/access.2019.2932146.
  • [12] Liu, T., Ye, X. and Sun, B., (2019), “Combining Convolutional Neural Network and Support Vector Machine for Gait-based Gender Recognition,” Proc. 2018 Chinese Autom. Congr. CAC 2018, pp. 3477–3481, doi: 10.1109/CAC.2018.8623118.
  • [13] Akbulut, Y., Şengür, A. and Ekici, S., (2017), “Gender recognition from face images with deep learning,” Int. Artif. Intell. Data Process. Symp., pp. 1–4.
  • [14] Viedma, I. and Tapia, J., (2018), “Deep Gender Classification and Visualization of Near-Infra-Red Periocular-Iris images,” IEEE 3rd Int. Conf. Image Process. Appl. Syst. IPAS, pp. 73–78, 2018, doi: 10.1109/IPAS.2018.8708857.
  • [15] Van De Wolfshaar, J., Karaaba, M. F. and Wiering, M. A., (2015), “Deep convolutional neural networks and support vector machines for gender recognition,” Proc. - 2015 IEEE Symp. Ser. Comput. Intell. SSCI 2015, pp. 188–195, doi: 10.1109/SSCI.2015.37.
  • [16] Afifi, M., (2019), “11K Hands: Gender recognition and biometric identification using a large dataset of hand images,” Multimed. Tools Appl., vol. 78, no. 15, pp. 20835–20854, doi: 10.1007/s11042-019-7424-8.
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  • [21] Vishwesh, S., (2019), “PyTorch for Beginners: Image Classification using Pre-trained models,” https://www.learnopencv.com/pytorch-for-beginners-image-classification-using-pre-trained-models/ (accessed May 26, 2020).
  • [22] Çarkacı, N.,(2018), “Derin Öğrenme Uygulamalarında En Sık kullanılan Hiper-parametreler,” https://medium.com/deep-learning-turkiye/derin-ogrenme-uygulamalarinda-en-sik-kullanilan-hiper-parametreler-ece8e9125c4 (accessed May 26, 2020).
  • [23] Antipov, G., Berrani, S. A. and Dugelay J.L., (2016), “Minimalistic CNN-based ensemble model for gender prediction from face images,” Pattern Recognit. Lett., vol. 70, pp. 59–65, doi: 10.1016/j.patrec.2015.11.011.
  • [24] Juefei-Xu, F., Verma, E., Goel, P., Cherodian, A. and Savvides, M., (2016) “DeepGender: Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Convolutional Neural Networks with Attention,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 136–145, doi: 10.1109/CVPRW.2016.24.
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  • [26] Kabasakal B. and Sumer, E., (2018), “Gender recognition using innovative pattern recognition techniques,” 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, doi: 10.1109/SIU.2018.8404306.
  • [27] Nistor, S. C., Marina, A. C., Darabant, a. S. and Borza, D.,( 2017), “Automatic gender recognition for ‘in the wild’ facial images using convolutional neural networks,” Proc. - 2017 IEEE 13th Int. Conf. Intell. Comput. Commun. Process. ICCP 2017, pp. 287–291, doi: 10.1109/ICCP.2017.8117018.
  • [28] Afifi, M., (2019), “11K Hands: Gender recognition and biometric identification using a large dataset of hand images,” Multimed. Tools Appl., vol. 78, no. 15, pp. 20835–20854, doi: 10.1007/s11042-019-7424-8.
  • [29] Sırma, K., (2020), “Fingertip Images,” https://www.kaggle.com/keremsirma/fingertip-images/ (accessed Aug 1, 2020).

GENDER ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS USING FINGERTIP IMAGES

Year 2020, Issue: 045, 111 - 125, 31.12.2020

Abstract

Bringing several innovations to our daily life, the importance of artificial intelligence technology has been increasing day by day and has created new fields for researchers. Gender classification is also an important research topic in the field of artificial intelligence. Studies on gender prediction from face, body, and even fingerprint images have been done. Also, today, biometric recognition systems have reached levels that can determine people's fingerprints, face, iris, palm prints, signature, DNA, and retina. In this study, various models were trained and tested on gender classification from fingertip images. In the, a ready dataset was not used and finger images were collected from more than 200 people. Rotation, cutting, and background reduction are applied to the collected images and made ready for the training. 4 different network models were set in the fieldwork. Data augmentation and transfer learning were used in these models. Working in a limited area, the model we created has achieved high-performance results, for all that the quality and angles of each image are different. The model proposed in this study has a performance rate of 86.39%.

References

  • [1] Apaydın, E., (2004), Introduction to Machine Learning (Adaptive Computation and Machine Learning). MIT Press
  • [2] Bishop, C. M., (2006) Pattern Recognition and Machine Learning. Springer Science + Business Media.
  • [3] Levi G. and Hassncer, T., (2015), “Age and gender classification using convolutional neural networks,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2015-Octob, pp. 34–42, doi: 10.1109/CVPRW.2015.7301352.
  • [4] Eidinger, E., Enbar, R., and Hassner, T.,(2014), “Age and gender estimation of unfiltered faces,” IEEE Trans. Inf. Forensics Secur., vol. 9, no. 12, pp. 2170–2179, , doi: 10.1109/TIFS.2014.2359646.
  • [5] Cengil, E., Çinar, A., and Güler, Z., (2017), “A GPU-based convolutional neural network approach for image classification,” in International Artificial Intelligence and Data Processing Symposium (IDAP).
  • [6] Abdulkadir, S., Akbulut, Y., Guo, Y., and Bajaj, V., (2017) “Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm,” Heal. Inf Sci Syst.
  • [7] Girshick, R., Donahue, J., Darrell, T., and Malik, J., (2014), “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 580–587, doi: 10.1109/CVPR.2014.81.
  • [8] Jialin Pan, S., and Yang, Q., (2010) “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng., vol. 22, pp. 1345–1359.
  • [9] Koçer, B., (2012) “Transfer Öğrenmede Yeni Yaklaşımlar,” Selçuk University.
  • [10] Illouz, E., David, E. and Netanyahu, N.S., (2019), “Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks,” vol. 11141, no. October, pp. 613–621, doi: 10.1007/978-3-030-01424-7_60.
  • [11] Baek, N. R., Cho, S. W., Koo, J. H., Truong, N. Q. and Park, K. R.,(2019), “Multimodal Camera-Based Gender Recognition Using Human-Body Image With Two-Step Reconstruction Network,” IEEE Access, vol. 7, pp. 104025–104044, doi: 10.1109/access.2019.2932146.
  • [12] Liu, T., Ye, X. and Sun, B., (2019), “Combining Convolutional Neural Network and Support Vector Machine for Gait-based Gender Recognition,” Proc. 2018 Chinese Autom. Congr. CAC 2018, pp. 3477–3481, doi: 10.1109/CAC.2018.8623118.
  • [13] Akbulut, Y., Şengür, A. and Ekici, S., (2017), “Gender recognition from face images with deep learning,” Int. Artif. Intell. Data Process. Symp., pp. 1–4.
  • [14] Viedma, I. and Tapia, J., (2018), “Deep Gender Classification and Visualization of Near-Infra-Red Periocular-Iris images,” IEEE 3rd Int. Conf. Image Process. Appl. Syst. IPAS, pp. 73–78, 2018, doi: 10.1109/IPAS.2018.8708857.
  • [15] Van De Wolfshaar, J., Karaaba, M. F. and Wiering, M. A., (2015), “Deep convolutional neural networks and support vector machines for gender recognition,” Proc. - 2015 IEEE Symp. Ser. Comput. Intell. SSCI 2015, pp. 188–195, doi: 10.1109/SSCI.2015.37.
  • [16] Afifi, M., (2019), “11K Hands: Gender recognition and biometric identification using a large dataset of hand images,” Multimed. Tools Appl., vol. 78, no. 15, pp. 20835–20854, doi: 10.1007/s11042-019-7424-8.
  • [17] Barbosa, I.B., Theoharis, T., Schellewald, C. and Athwal, C., (2013)., “Transient biometrics using fingernails,” IEEE 6th Int. Conf. Biometrics Theory, Appl. Syst. BTAS 2013, doi: 10.1109/BTAS.2013.6712730.
  • [18] Ceyhan E.B. and Sağıroğlu, Ş., (2019), “A New Intelligent System for Predicting Gender from Fingerprint,” Düzce Univ. J. Sci. Technol., vol. 4, pp. 25–36, doi: 0.29130/dubited.457914.
  • [19] Liu, X., Li , J., Pan, J.-S., and Hu, C. (2017). Deep convolutional neural networks-based age and gender classification with facial images. 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS).Matlab Documentation. (2019). MathWorks.
  • [20] Er, M. B:, (2019), “Emotıon Analysıs In Turkısh Musıc Wıth Machıne Learnıng,” Maltepe University.
  • [21] Vishwesh, S., (2019), “PyTorch for Beginners: Image Classification using Pre-trained models,” https://www.learnopencv.com/pytorch-for-beginners-image-classification-using-pre-trained-models/ (accessed May 26, 2020).
  • [22] Çarkacı, N.,(2018), “Derin Öğrenme Uygulamalarında En Sık kullanılan Hiper-parametreler,” https://medium.com/deep-learning-turkiye/derin-ogrenme-uygulamalarinda-en-sik-kullanilan-hiper-parametreler-ece8e9125c4 (accessed May 26, 2020).
  • [23] Antipov, G., Berrani, S. A. and Dugelay J.L., (2016), “Minimalistic CNN-based ensemble model for gender prediction from face images,” Pattern Recognit. Lett., vol. 70, pp. 59–65, doi: 10.1016/j.patrec.2015.11.011.
  • [24] Juefei-Xu, F., Verma, E., Goel, P., Cherodian, A. and Savvides, M., (2016) “DeepGender: Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Convolutional Neural Networks with Attention,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 136–145, doi: 10.1109/CVPRW.2016.24.
  • [25] Karakaş, E., (2019), “Derin Sinir Ağlarının Füzyonu ile Yüz İmgelerinden Yaş Grubu ve Cinsiyet Sınıflandırma,” Atatürk University.
  • [26] Kabasakal B. and Sumer, E., (2018), “Gender recognition using innovative pattern recognition techniques,” 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, doi: 10.1109/SIU.2018.8404306.
  • [27] Nistor, S. C., Marina, A. C., Darabant, a. S. and Borza, D.,( 2017), “Automatic gender recognition for ‘in the wild’ facial images using convolutional neural networks,” Proc. - 2017 IEEE 13th Int. Conf. Intell. Comput. Commun. Process. ICCP 2017, pp. 287–291, doi: 10.1109/ICCP.2017.8117018.
  • [28] Afifi, M., (2019), “11K Hands: Gender recognition and biometric identification using a large dataset of hand images,” Multimed. Tools Appl., vol. 78, no. 15, pp. 20835–20854, doi: 10.1007/s11042-019-7424-8.
  • [29] Sırma, K., (2020), “Fingertip Images,” https://www.kaggle.com/keremsirma/fingertip-images/ (accessed Aug 1, 2020).
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Kerem Sırma This is me 0000-0003-2902-1617

Pakize Erdoğmuş This is me 0000-0003-2172-5767

Publication Date December 31, 2020
Submission Date May 29, 2020
Published in Issue Year 2020 Issue: 045

Cite

IEEE K. Sırma and P. Erdoğmuş, “GENDER ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS USING FINGERTIP IMAGES”, JSR-A, no. 045, pp. 111–125, December 2020.