Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

Authors

DOI:

10.33395/sinkron.v7i4.11887

Keywords:

Convolutional Neural Network, InceptionV3, MobileNetV2, Gender Classification

Abstract

Convolutional neural network (CNN) is one of the neural networks used in image data. CNN has a good ability to detect objects in an image. This study discusses the comparison of two deep learning models based on convolutional neural network, namely the Inception-V3 method and the MobileNet method. Both algorithms are analyzed fairly on gender classification using eye images. There have been many research completions that have conducted studies on gender classification based on faces, but gender classification based on eyes has many challenges. This gender classification is grouped into two classes, namely male and female. This study aims to build a gender classification model from eye image. The processes in this research include selecting the dataset, preprocessing the data, dividing the data which is divided into training data and test data, modeling, and evaluating the performance of the model. This study uses a public dataset, where the data contains a total of 2,681 images consisting of 1251 male eyes and 1430 female eyes. This study concludes that gender classification using eye image using the Inception-V3 method is better than the MobileNet method. This is obtained based on the accuracy value generated by the Inception-V3 method which is higher than the MobileNet-V2 method which obtains an accuracy of 91.82%.

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References

Chen, Thou-Ho, Ping-Hsueh Wu, and Yung-Chuen Chiou (2004). “An early fire- detection method based on image processing”. In: 2004 International Confer- ence on Image Processing, 2004. ICIP’04. Vol. 3. IEEE, pp. 1707–1710.

Chen, L.; Li, S.; Bai, Q.; Yang, J.; Jiang, S.; Miao, Y. Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens. 2021, 13, 4712. https://doi.org/10.3390/rs13224712

Ng, Choon Boon, Yong Haur Tay, and Bok-Min Goi (2012). “Recognizing human gender in computer vision: a survey”. In: Pacific Rim International Conference on Artificial Intelligence. Springer, pp. 335–346.

Zufar, Muhammad (2016). “Convolutional neural networks untuk pengenalan wa- jah secara real-time”. PhD thesis. Institut Technology Sepuluh Nopember.

Albawi, Saad, Tareq Abed Mohammed, and Saad Al-Zawi (2017). “Understand- ing of a convolutional neural network”. In: 2017 international conference on engineering and technology (ICET). Ieee, pp. 1–6.

Abdurrohman, Harits, Robih Dini, and Arief Purnama Muharram (2018). “Eval- uasi Performa metode Deep Learning untuk Klasifikasi Citra Lesi Kulit The HAM10000”. In: Seminar Nasional Instrumentasi, Kontrol dan Otomasi, pp. 63– 68.

Jeong, Yoosoo et al. (2018). “Accurate age estimation using multi-task siamese network-based deep metric learning for frontal face images”. In: Symmetry 10.9, p. 385.

Kabir, R.; Watanobe, Y.; Islam, M.R.; Naruse, K.; Rahman, M.M. Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud–Robot System. Sensors 2022, 22, 1352. https://doi.org/10.3390/s22041352

Rattani, Ajita, Narsi Reddy, and Reza Derakhshani (2018). “Convolutional neural networks for gender prediction from smartphone-based ocular images”. In: Iet Biometrics 7.5, pp. 423–430.

Sultana, Farhana, Abu Sufian, and Paramartha Dutta (2018). “Advancements in image classification using convolutional neural network”. In: 2018 Fourth In- ternational Conference on Research in Computational Intelligence and Com- munication Networks (ICRCICN). IEEE, pp. 122–129.

Wibowo, Suryo Adhi et al. (2018). “Collaborative learning based on convolutional features and correlation filter for visual tracking”. In: International Journal of Control, Automation and Systems 16.1, pp. 335–349.

Asriny, Dhiya Mahdi et al. (2019). “Implementasi Deep Learning Menggunakan Convolutional Neural Network Untuk Klasifikasi Citra Jeruk”. PhD thesis. Uni- versitas Islam Indonesia.

Cao, Wenzhi, Vahid Mirjalili, and Sebastian Raschka (2019). “Consistent rank logits for ordinal regression with convolutional neural networks”. In: arXiv preprint arXiv:1901.07884 6.

Savchenko, Andrey V (2019). “Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet”. In: PeerJ Computer Science 5, e197.

Setiawan, Wahyudi (2019). “Perbandingan arsitektur convolutional neural network untuk klasifikasi fundus”. In: Jurnal Simantec 7.2, pp. 48–53.

Torres, Edgar, Sergio L Granizo, and Myriam Hernandez-Alvarez (2019). “Gen- der and age classification based on human features to detect illicit activity insuspicious sites”. In: 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, pp. 416–419.

Alwanda, Muhammad Rafly, Raden Putra Kurniawan Ramadhan, and Derry Alam- syah (2020). “Implementasi Metode Convolutional Neural Network Menggu- nakan Arsitektur LeNet-5 untuk Pengenalan Doodle”. In: Jurnal Algoritme 1.1, pp. 45–56.

Darmatasia, Darmatasia (2020). “Analisis Perbandingan Performa Model Deep Learning Untuk Mendeteksi Penggunaan Masker”. In: JURNAL IT 11.2, pp. 65– 71.

Feriawan, Jimmy and Daniel Swanjaya (2020). “Perbandingan Arsitektur Visual Geometry Group dan MobileNet Pada Pengenalan Jenis Kayu”. In: Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi). Vol. 4. 3, pp. 185– 190.

Greco, Antonio et al. (2020). “A convolutional neural network for gender recogni- tion optimizing the accuracy/speed tradeoff”. In: IEEE Access 8, pp. 130771– 130781.

Hassan, Khaled Rahman and Israa Hadi Ali (2020). “Age and Gender Classification using Multiple Convolutional Neural Network”. In: IOP Conference Series: Materials Science and Engineering. Vol. 928. 3. IOP Publishing, p. 032039.

Pangestu, Ridho Aji, Basuki Rahmat, and Fetty Tri Anggraeny (2020). “Imple- mentasi algoritma CNN untuk klasifikasi citra lahan dan perhitungan luas”. In: Jurnal Informatika dan Sistem Informasi (JIFoSI) 1.1, pp. 166–174.

Roihan, Ahmad, Po Abas Sunarya, and Ageng Setiani Rafika (2020). “Peman- faatan Machine Learning dalam Berbagai Bidang”. In: IJCIT (Indonesian J. Comput. Inf. Technol. 5.1, pp. 75–82.

Sikumbang, Warnia Nengsih (2020). “CNN Modelling Untuk Deteksi Wajah Berba- sis Gender Menggunakan Python”. In: Jurnal Komputer Terapan 6.2, pp. 190– 199.

Siqueira, Henrique, Sven Magg, and Stefan Wermter (2020). “Efficient facial fea- ture learning with wide ensemble-based convolutional neural networks”. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 34. 04, pp. 5800– 5809.

Trivedi, Gangesh and Nitin N Pise (2020). “Gender classification and age estima- tion using neural networks: a survey”. In: International Journal of Computer Applications 975, p. 8887.

Wang, Kai et al. (2020). “Region attention networks for pose and occlusion robust facial expression recognition”. In: IEEE Transactions on Image Processing 29, pp. 4057–4069.

Zein, Afrizal (2020). “Memprediksi UsiaDan Jenis Kelamin Menggunakan Convo- lutional Neural Networks”. In: SAINSTECH: JURNAL PENELITIAN DAN PENGKAJIAN SAINS DAN TEKNOLOGI 30.1.

Benkaddour, Mohammed Kamel, Sara Lahlali, and Maroua Trabelsi (2021). “Hu- man Age and Gender Classification using Convolutional Neural Network”. In 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH). IEEE, pp. 215–220.

Greco, Antonio et al. (2021). “Gender recognition in the wild: a robustness evalu- ation over corrupted images”. In: Journal of Ambient Intelligence and Human- ized Computing 12.12, pp. 10461–10472.

Munarto, Ri and Ardian Darma (2021). “Klasifikasi Gender dan Usia Berdasarkan Citra Wajah Manusia Menggunakan Convolutional Neural Network”. In: Setrum: Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer 10.2.

Nufus, Nafisun et al. (2021). “Sistem Pendeteksi Pejalan Kaki Di Lingkungan Ter- batas Berbasis SSD MobileNet V2 Dengan Menggunakan Gambar 360° Ter- normalisasi”. In: Prosiding Seminar Nasional Sains Teknologi dan Inovasi In- donesia (SENASTINDO). Vol. 3, pp. 123–134.

Savchenko, Andrey V (2021). “Facial expression and attributes recognition based on multi-task learning of lightweight neural networks”. In: 2021 IEEE 19th In- ternational Symposium on Intelligent Systems and Informatics (SISY). IEEE, pp. 119–124.

Supriadi, Muhammad Fadhlan, Ema Rachmawati, and Anditya Arifianto (2021). “Pembangunan Aplikasi Mobile Pengenalan Objek Untuk Pendidikan Anak Usia Dini”. In: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) 8.2.

Udayana, I Putu Agus Eka Darma and I Kadek Dwi Gandika Supartha (2021). “Implementasi Kombinasi Metode Mean Denoising dan Convolutional Neural Network pada Facial Landmark Detection”. In: Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI 10.1, pp. 1–10

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How to Cite

Listio, S. W. P. . (2022). Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2593-2601. https://doi.org/10.33395/sinkron.v7i4.11887