Detection of Signs of Ageing in Face and Demanding Situations to the Body and Skin Diseases after COVID -19 using Machine Learning
DOI:
https://doi.org/10.32628/IJSRSET229142Keywords:
Skin Disease, Advanced Computer Vision, Convolutional Neural Networks (CNN), Google Net Architecture, Tensorflow, Ageing Detection, Object Detection API.Abstract
The primary goal of this thesis is to simulate the causes of human face ageing and to detect skin illnesses. This project combines the capabilities of a Deep Learning model, notably EfficientDet, with the capabilities of a machine learning model and Advanced Computer Vision to identify and locate aging and skin disease. In an uploaded photo there are irregularities which can be detected. The appearance of age-related face changes is determined by a variety of factors. Wrinkles, dark patches, and swollen eyes are all variables to consider. The TensorFlow Objection Detection API is used to investigate the factors. TensorFlow Zoo's EfficientDet model is pre-trained. The proposed models are found to be effective based on the outcomes. Very effective in predicting the indications of ageing in people of all ages. Python was used to implement this project.
References
- O. F. Osman and M. H. Yap, "Computational Intelligence in Automatic Face Age Estimation: A Survey," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 3, pp. 271-285, June 2019, doi: 10.1109/TETCI.2018.2864554.
- H. S. A. Qasim, M. Shahzad and M. M. Fraz, "Deep Learning for Face Detection: Recent Advancements," 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), 2021, pp. 1-6, doi: 10.1109/ICoDT252288.2021.9441476.
- S. Sharma, M. Bhatt and P. Sharma, "Face Recognition System Using Machine Learning Algorithm," 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020, pp. 1162-1168, doi: 10.1109/ICCES48766.2020.9137850.
- C. A. Mattmann and Z. Zhang, "Deep Facial Recognition using Tensorflow," 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 2019, pp. 45-51, doi: 10.1109/DLS49591.2019.00011.
- M. Nara, B. R. Mukesh, P. Padala and B. Kinnal, "Performance Evaluation of Deep Learning frameworks on Computer Vision problems," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019, pp. 670-674, doi: 10.1109/ICOEI.2019.8862603.
- S. Kumar and M. Kumar, "A Study on the Image Detection Using Convolution Neural Networks and TenserFlow," 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 2018, pp. 1080-1083, doi: 10.1109/ICIRCA.2018.8597204
- M. Tan, R. Pang and Q. V. Le, "EfficientDet: Scalable and Efficient Object Detection," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 10778-10787, doi: 10.1109/CVPR42600.2020.01079.
- https://www.youtube.com/watch?v=cvyDYdI2nEI&ab_channel=GilbertTanner
- https://www.tensorflow.org/hub/tutorials/object_detection
- https://towardsdatascience.com/a-thorough-breakdown-of-efficientdet-for-object-detection-dc6a15788b73
- https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
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