Face Mask Image Classification using VGG16

Deskripsi Dataset

Dataset terdiri dari 2 class yaitu with_mask dan without_mask

• Jumlah dataset Training : 5000

• Jumlah dataset Validasi : 2553

• Jumlah train kelas Masker : 2500

• Jumlah train kelas Non Masker : 2500

• Jumlah validasi kelas Masker : 1225

• Jumlah validasi kelas Non Masker : 1328

Content

• Kumpulan data terdiri dari 7553 gambar RGB dalam 2 folder sebagai withmask dan tanpamask. Gambar disebut sebagai label dengan masker dan tanpa masker. Gambar wajah dengan topeng adalah 3725 dan gambar wajah tanpa topeng adalah 3828.

Acknowledgements

• Saya telah mengambil 1776 gambar termasuk gambar With and Without Face Mask dari akun Github Prajna Bhandary https://github.com/prajnasb/observations Sisa 5777 gambar dikumpulkan dan disaring dari mesin pencari Google.

• 3725 Gambar Wajah dengan Masker

• 3828 Gambar Wajah tanpa Masker.

Inspiration

• Terinspirasi oleh Adrian Rosebrock, PhD. Dengan bantuan bimbingannya di situs webnya https://www.pyimagesearch.com/ mudah untuk memahami dasar-dasar pemrosesan Gambar dan Deep Learning.

Paper Referensi Utama

• Naufal, M. F., Kusuma, S. F., Prayuska, Z. A., Yoshua, A. A., Lauwoto, Y. A., Dinata, N. S., & Sugiarto, D. (2021). “Comparative Analysis of Image Classification Algorithms for Face Mask Detection”. Journal of Information Systems Engineering and Business Intelligence, 7(1), 56. https://doi.org/10.20473/jisebi.7.1.56-66
  

Paper Referensi Pendukung

• Zeiler, M. D., & Fergus, R. (2014). “Visualizing and Understanding Convolutional Networks” (pp. 818–833). https://doi.org/10.1007/978-3-319-10590-1_53
  

• Pak, M., & Kim, S. (2017). “A review of deep learning in image recognition”. 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), 1–3. https://doi.org/10.1109/CAIPT.2017.8320684

• Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., & Hemanth, J. (2021). “SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2”. Sustainable Cities and Society, 66, 102692. https://doi.org/10.1016/j.scs.2020.102692

• O’Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G. V., Krpalkova, L., Riordan, D., & Walsh, J. (2020). “Deep Learning vs. Traditional Computer Vision” (pp. 128–144). https://doi.org/10.1007/978-3-030-17795-9_10

• Huan, E.-Y., & Wen, G.-H. (2020). “Transfer learning with deep convolutional neural network for constitution classification with face image”. Multimedia Tools and Applications, 79(17–18), 11905–11919. https://doi.org/10.1007/s11042-019-08376-5

• Oumina, A., el Makhfi, N., & Hamdi, M. (2020). “Control The COVID-19 Pandemic: Face Mask Detection Using Transfer Learning”. 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), 1–5. https://doi.org/10.1109/ICECOCS50124.2020.9314511

• Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021). “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic”. Measurement, 167, 108288. https://doi.org/10.1016/j.measurement.2020.108288

• Postalcıoğlu, S. (2020). “Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition”. International Journal of Pattern Recognition and Artificial Intelligence, 34(02), 2051003. https://doi.org/10.1142/S0218001420510039

• Lou, G., & Shi, H. (2020). “Face image recognition based on convolutional neural network”. China Communications, 17(2), 117–124. https://doi.org/10.23919/JCC.2020.02.010

• Rokhana, R., Herulambang, W., & Indraswari, R. (2021). “Multi-Class Image Classification Based on MobileNetV2 for Detecting the Proper Use of Face Mask.” 2021 International Electronics Symposium (IES), 636–641. https://doi.org/10.1109/IES53407.2021.9594022

• Saranya, G., Sarkar, D., Ghosh, S., Basu, L., Kumaran, K., & Ananthi, N. (2021). “Face Mask Detection using CNN”. 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 426–431. https://doi.org/10.1109/CSNT51715.2021.9509556

• Jignesh Chowdary, G., Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). “Face Mask Detection Using Transfer Learning of InceptionV3” (pp. 81–90). https://doi.org/10.1007/978-3-030-66665-1_6

• Qin, B., & Li, D. (2020). “Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19”. Sensors, 20(18), 5236. https://doi.org/10.3390/s20185236

• B, S., Lesle A, A., Diwakar, B., R, K., & M, G. (2020). “Evaluating Performance of Deep Learning Architectures for Image Classification”. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 917–922. https://doi.org/10.1109/ICCES48766.2020.9137884

Cara menggunakan Program

• Pull / Clone project dari github
• Buka File ipynb
• Run (bisa Buka di Notebook anda atau google colabolatory)

Teknik Deep Learning yang digunakan

• Model dengan menggunakan algoritma VGG16

Authors

Kontributors dalam project ini adalah :

• R Aldien Prayoga 201910370311413

• Aris Muhandisin 201910370311432