Conference Paper
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Gender Classification from Eye Images by Using Pretrained Convolutional Neural Networks

Year 2021, Volume: 14 , 39 - 44, 31.12.2021

Yucel Cımtay Gokce Nur Yılmaz

https://doi.org/10.55549/epstem.1050171

Abstract

Automatic gender classification from face images has been a popular topic among researchers for a decade. Feature extraction and classification methods are very important to create a successful automatic classification system. Due to the richness of face image datasets today, many successful machine learning and deep learning methods has been implemented. It is very critical to extract accurate features from the datasets to achieve promising classification scores when traditional machine learning methods are used. However, deep learning models have been designed to extract the features automatically from the raw data directly. This also automatize the feature extraction process besides classification. The hidden and unpredictable feature sets can be explored by the deep neural networks which can increase the classification performance comparing to traditional machine learning methods. Convolutional Neural Networks (CNN) as one of the effective classes of deep models have been adopted by many scientists for solving the gender classification problem. It can solve the problem of the fact that facial cues can change from origin to origin which makes the accurate feature extraction harder. There are several state-of-the art pretrained CNN structures which are very successful for image classification problems. The performance of CNNs is generally higher when the number of the input data is high. However, in this study, the success of the pretrained CNN models is investigated when the data is limited. Considering this fact, in this study, rather than using complete face images, only the one eye image regions with eyebrows are used for the gender classification. The performance results present that the best CNN models are NASNetLarge and Xception models.

Keywords

Eye region facial data Classification with CNN Layer structure

References

  • Abdalrady, N. A., & Aly, S. (2020, February). Fusion of multiple simple convolutional neural networks for gender classification. In 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), 251-256. IEEE.
  • Akbulut, Y., Şengür, A., & Ekici, S. (2017, September). Gender recognition from face images with deep learning. In 2017 International artificial intelligence and data processing symposium (IDAP), 1-4. IEEE.
  • Arora, S., & Bhatia, M. P. S. (2018, July). A robust approach for gender recognition using deep learning. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT),1-6. IEEE.
  • Cimtay, Y., Alkan, B., & Demirel, B. (2021). Fingerprint Pattern Classification by Using Various Pre-Trained Deep Neural Networks. Avrupa Bilim ve Teknoloji Dergisi, (24), 258-261.
  • Color-feret-database. (12 October 2021). https://www.nist.gov/itl/products-and-services/
  • Danisman, T., Bilasco, I. M., & Martinet, J. (2015). Boosting gender recognition performance with a fuzzy inference system. Expert Systems with Applications, 42(5), 2772-2784.
  • Deng, Y., Luo, P., Loy, C. C., & Tang, X. (2014, November). Pedestrian attribute recognition at far distance. In Proceedings of the 22nd ACM international conference on Multimedia,789-792.
  • Eidinger, E., Enbar, R., & Hassner, T. (2014). Age and gender estimation of unfiltered faces. in IEEE Transactions on Information Forensics and Security, 9 (12), 2170-2179, doi: 10.1109/TIFS.2014.2359646.
  • Eyes-rtte. (23 September 2021). https://www.kaggle.com/pavelbiz/eyes-rtte
  • Gallagher, A. C., & Chen, T. (2008, June). Clothing cosegmentation for recognizing people. In 2008 IEEE conference on computer vision and pattern recognition, 1-8. IEEE.
  • Gündüz, G., & Cedimoğlu, İ. H. (2019). Derin Öğrenme Algoritmalarını Kullanarak Görüntüden Cinsiyet Tahmini. Sakarya University Journal of Computer and Information Sciences, 2(1), 9-17.
  • Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Univ. Massachusetts, Amherst, MA, USA.
  • Janahiraman, T. V., & Subramaniam, P. (2019, October). Gender Classification Based on Asian Faces using Deep Learning. In 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET) (84-89). IEEE.
  • Keras applications. (20 October 2021). https://keras.io/api/applications/
  • Levi, G., & Hassner, T. (2015). Age and gender classification using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (34-42).
  • PedestrianData (22 October 2021). http://cbcl.mit.edu/software-datasets/PedestrianData.html
  • Ranjan, R., Patel, V. M., & Chellappa, R. (2017). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE transactions on pattern analysis and machine intelligence, 41(1), 121-135.
  • Raza, M., Sharif, M., Yasmin, M., Khan, M. A., Saba, T., & Fernandes, S. L. (2018). Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Future Generation Computer Systems, 88, 28-39.
  • Yu, Z., Shen, C., & Chen, L. (2017, December). Gender classification of full body images based on the convolutional neural network. In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 707-711. IEEE.

Year 2021, Volume: 14 , 39 - 44, 31.12.2021

Yucel Cımtay Gokce Nur Yılmaz

https://doi.org/10.55549/epstem.1050171

Abstract

References

  • Abdalrady, N. A., & Aly, S. (2020, February). Fusion of multiple simple convolutional neural networks for gender classification. In 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), 251-256. IEEE.
  • Akbulut, Y., Şengür, A., & Ekici, S. (2017, September). Gender recognition from face images with deep learning. In 2017 International artificial intelligence and data processing symposium (IDAP), 1-4. IEEE.
  • Arora, S., & Bhatia, M. P. S. (2018, July). A robust approach for gender recognition using deep learning. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT),1-6. IEEE.
  • Cimtay, Y., Alkan, B., & Demirel, B. (2021). Fingerprint Pattern Classification by Using Various Pre-Trained Deep Neural Networks. Avrupa Bilim ve Teknoloji Dergisi, (24), 258-261.
  • Color-feret-database. (12 October 2021). https://www.nist.gov/itl/products-and-services/
  • Danisman, T., Bilasco, I. M., & Martinet, J. (2015). Boosting gender recognition performance with a fuzzy inference system. Expert Systems with Applications, 42(5), 2772-2784.
  • Deng, Y., Luo, P., Loy, C. C., & Tang, X. (2014, November). Pedestrian attribute recognition at far distance. In Proceedings of the 22nd ACM international conference on Multimedia,789-792.
  • Eidinger, E., Enbar, R., & Hassner, T. (2014). Age and gender estimation of unfiltered faces. in IEEE Transactions on Information Forensics and Security, 9 (12), 2170-2179, doi: 10.1109/TIFS.2014.2359646.
  • Eyes-rtte. (23 September 2021). https://www.kaggle.com/pavelbiz/eyes-rtte
  • Gallagher, A. C., & Chen, T. (2008, June). Clothing cosegmentation for recognizing people. In 2008 IEEE conference on computer vision and pattern recognition, 1-8. IEEE.
  • Gündüz, G., & Cedimoğlu, İ. H. (2019). Derin Öğrenme Algoritmalarını Kullanarak Görüntüden Cinsiyet Tahmini. Sakarya University Journal of Computer and Information Sciences, 2(1), 9-17.
  • Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Univ. Massachusetts, Amherst, MA, USA.
  • Janahiraman, T. V., & Subramaniam, P. (2019, October). Gender Classification Based on Asian Faces using Deep Learning. In 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET) (84-89). IEEE.
  • Keras applications. (20 October 2021). https://keras.io/api/applications/
  • Levi, G., & Hassner, T. (2015). Age and gender classification using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (34-42).
  • PedestrianData (22 October 2021). http://cbcl.mit.edu/software-datasets/PedestrianData.html
  • Ranjan, R., Patel, V. M., & Chellappa, R. (2017). Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE transactions on pattern analysis and machine intelligence, 41(1), 121-135.
  • Raza, M., Sharif, M., Yasmin, M., Khan, M. A., Saba, T., & Fernandes, S. L. (2018). Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Future Generation Computer Systems, 88, 28-39.
  • Yu, Z., Shen, C., & Chen, L. (2017, December). Gender classification of full body images based on the convolutional neural network. In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 707-711. IEEE.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Yucel Cımtay

Gokce Nur Yılmaz

Publication Date December 31, 2021
Published in Issue Year 2021Volume: 14

Cite

APA Cımtay, Y., & Yılmaz, G. N. (2021). Gender Classification from Eye Images by Using Pretrained Convolutional Neural Networks. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 14, 39-44. https://doi.org/10.55549/epstem.1050171
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