International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Classification Facial Skin and Treatment Suggestions for Good Skin Using Deep Learning with Region of Interest (ROI) Patches

Author : Satwik Sunnam ¹ Avuku Obulesh ²

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Date of Publication :15th September 2022

Abstract: Skin is the outermost layer of the human body. It serves as a protective layer for the internal organs of the human body. We should keep that healthy by knowing the respective skin type of ourselves rather than using different skin type products for skin without knowing our skin type can lead to damage to skin cells and maybe be responsible for epidermal diseases. There are four different skin types: Normal skin, Oily skin, dry skin, combination skin. We have taken two skin types: Normal skin, Oily skin of faces. The main objective of this paper is to classify facial skin as normal skin or oily skin. After classification, this paper suggests treatment for respective skin. For this task we used deep learning and open CV. In order to enhance performance, we exploit knowledge related to the human face structure. We train our model by employing automatically created facial regions of interest (ROI) to this end. By jointly learning the network parameters and optimized network output combination weights, each facial region appropriately contributes to the final classification result

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14. Zuo, H., Fan, H., Blasch, E., & Ling, H. (2017). Combining Convolutional and Recurrent Neural Networks for Human Skin Detection. IEEE Signal Processing Letters, 24(3), 289–293.
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1. Hsu, R. L., Abdel-Mottaleb, M., & Jain, A. K. (2002). Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence.
2. De-La-Torre, M., Granger, E., Radtke, P. V. W., Sabourin, R., & Gorodnichy, D. O. (2015). Partially -supervised learning from facial trajectories for face recognition in video surveillance. Information Fusion.
3. Argyros, A. A., & Lourakis, M. I. A. (2004). Real-time tracking of multiple skin-colored objects with a possibly moving camera. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
4. Roy, K., Mohanty, A., & Sahay, R. R. (2017). Deep Learning Based Hand Detection in Cluttered Environment Using Skin Segmentation. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 640–649.
5. Han, J., Award, G. M., Sutherland, A., & Wu, H. (2006). Automatic skin segmentation for gesture recognition combining region and support vector machine active learning. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 237–242.
6. Sang, H., Ma, Y., & Huang, J. (2013). Robust Palmprint Recognition Base on Touch-Less Color Palmprint Images Acquired. Journal of Signal and Information Processing, 04(02), 134–139.
7. Lee, J.-S., Kuo, Y.-M., Chung, P.-C., & Chen, E.-L. (2007). Naked image detection based on adaptive and extensible skin color model. Pattern Recognition, 40, 2261–2270.
8. C. Prema, & Manimegalai, M. (2012). Survey on Skin Tone Detection using Color Spaces. International Journal of Applied Information Systems (IJAIS), 2(2), 18–26.
9. Kakumanu, P., Makrogiannis, S., & Bourbakis, N. (2007). A survey of skin-color modeling and detection methods. Pattern Recognition, 40(3), 1106–1122.
10. Chen, W., Wang, K., Jiang, H., & Li, M. (2016). Skin color modeling for face detection and segmentation: a review and a new approach. Multimedia Tools and Applications, 75(2), 839–862
11. Mahmoodi, M. R., & Sayedi, S. M. (2016). A Comprehensive Survey on Human Skin Detection. International Journal of Image, Graphics and Signal Processing (IJIGSP), 8(5), 1–35.
12. Naji, S., Jalab, H. A., & Kareem, S. A. (2018). A survey on skin detection in colored images. Artificial Intelligence Review.
13. Xu, T., Zhang, Z., & Wang, Y. (2015). Patch-wise skin segmentation of human body parts via deep neural networks. Journal of Electronic Imaging, 24(4), 043009.
14. Zuo, H., Fan, H., Blasch, E., & Ling, H. (2017). Combining Convolutional and Recurrent Neural Networks for Human Skin Detection. IEEE Signal Processing Letters, 24(3), 289–293.
15. Kim, Y., Hwang, I., & Cho, N. I. (2017b). Convolutional neural networks and training strategies for skin detection. IEEE International Conference on Image Processing (ICIP), 3919–3923.
16. Ma, C., & Shih, H. (2018). Human Skin Segmentation Using Fully Convolutional Neural Networks. IEEE 7th Global Conference on Consumer Electronics (GCCE), 168–170.