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Joint Roughness and Wrinkle Detection Using Gabor Filtering and Dynamic Line Tracking
Elliot Naylor, Marius Miknis, Ross Davies
Pages - 211 - 220     |    Revised - 30-09-2019     |    Published - 31-10-2019
Volume - 13   Issue - 5    |    Publication Date - October 2019  Table of Contents
Mobile Devices, Wrinkle, Roughness, Texture Analysis, Line Tracking.
Skin quality analysis is a continuously increasing field for the detection of skin dryness, wrinkles and other skin conditions for the recommendation of skin-care products. Each method reveals valuable information on age, disease and general habits. Contributions are being made from companies, but many methods make use of extensive image data and diagnostic equipment that are too expensive to obtain. These would involve people visiting skin-care shops for a diagnosis, which is often time consuming and troublesome. A joint technique is proposed to locate skin roughness as part of an optimized wrinkle detection technique based on the capabilities of mobile devices without the use of diagnostic tools. The technique makes use of thresholding, shape analysis and dynamic line tracking for the analysis of skin roughness and wrinkles.
1 Google Scholar 
2 refSeek 
3 Doc Player 
4 Scribd 
5 SlideShare 
A., M. (2007). Tobacco smoke causes premature skin aging. Journal of Dermatological Science, 48(3), 169-175.
Bae, J. S., Lee, S. H., Choi, K. S., & Kim, J. O. (2017). Robust skin-roughness estimation based on co-occurrence matrix. Journal of Visual Communication and Image Representation, 46, 13-22.
Batool, N., & Chellappa, R. (2015). Fast detection of Facial Wrinkles Based on Gabor Features Using Image Morphology and Geometric Constraints. Pattern Recognition, 48(3).
Callaghan, T., & Wilhelm, K. (2008). A review of ageing and an examination of clinical methods in the assessment of ageing skin. Part 2: Clinical perspectives and clinical methods in the evaluation of ageing skin. International Journal of Cosmetic Science, 30(5), 323-332.
Cavalcanti, P. G., Yari, Y., & Scharcanski, J. (2010). Pigmented skin lesion segmentation on macroscopic images. International Conference Image and Vision Computing New Zealand.
Chen, S., Liu, Y., Gao, X., & Han, Z. (2018). MobileFaceNets: Efficient CNNs for accurate real-time face verification on mobile devices. Biometric Recognition, 428-438.
Cho, M., Lee, D. H., Doh, E. J., Kim, Y., Chung, J. H., Kim, H. C., & Kim, S. (2017). Development and clinical validation of a novel photography-based skin erythema evaluation system: a comparison with the calculated consensus of dermatologists. International Journal of Cosmetic Science, 39(4), 426-434.
Choi, Y. H., Kim, D., Hwang, E., & Kim, B. J. (2014). Skin texture aging trend analysis using dermoscopy images. Skin Research and Technology, 20(4), 486-497.
Cula, G. O., Bargo, P. R., Nkengne, A., & Kollias, N. (2013). Assessing facial wrinkles: Automatic detection and quantification. Skin Research and Technology, 19(1).
Deshmukh, A., Pawar, S., & Joshi, M. (2013). Feature level fusion of face and fingerprint modalities using Gabor filter bank. 2013 IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2013.
Egawa, M., Oguri, M., Kuwahara, T., & Takahashi, M. (2002). Effect of exposure of human skin to a dry environment. Skin Research and Technology, 8(4), 212-218.
Fisher, R., Perkins, S., Walker, A., & Wolfart, E. (2003). Spatial Filters - Laplacian of Gaussian. Retrieved from http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998). Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention MICCAI98, 130137.
Fu, W., Breininger, K., Würfl, T., Ravikumar, N., Schaffert, R., & Maier, A. (2017). Frangi-Net: A Neural Network Approach to Vessel Segmentation. Bildverarbeitung Für Die Medizin, 341-346.
Fu, Y., & Huang, T. S. (2008). Human Age Estimation With Regression on Discriminative Aging Manifold. IEEE Transactions on Multimedia, 10(4), 578-584.
Gallagher, P. (2012). Smart-Phones Get Even Smarter Cameras. IEEE Consumer Electronics Magazine, 1(1), 25-30.
Jeong, S. G., Tarabalka, Y., & Zerubia, J. (2014). Marked point process model for facial wrinkle detection. 2014 IEEE International Conference on Image Processing, ICIP 2014, 1391-1394.
Kostovic, K., & Lipozencic, J. (2004). Skin diseases in alcoholics. Acta Dermatovenerologica Croatica, 12(3), 181-190.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Ferrari, V. (2018). The Open Images Dataset V4: Unified image classification, object detection and visual relationship detection at scale. International Journal of Computer Vision.
Li, C., Lin, S., Zhou, K., & Ikeuchi, K. (2017). Specular highlight removal in facial images. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 2780-2789.
Ma, L., Huang, K., Yan, J., Wu, K., & Zhu, L. (2010). Boundary roughness analysis of skin lesions using local fractals and wavelet transforms. 2010 4th International Conference on Bioinformatics and Biomedical Engineering, ICBBE 2010.
Ng, C. C., Yap, M. H., Cheng, Y. T., & Hsu, G. S. (2018). Hybrid Ageing Patterns for face age estimation. Image and Vision Computing, 69, 92-102.
Ng, C. C., Yap, M. H., Costen, N., & Li, B. (2014). Automatic wrinkle detection using hybrid Hessian filter. 12th Asian Conference on Computer Vision, 9005, 609-622.
Ng, C. C., Yap, M. H., Costen, N., & Li, B. (2015). Wrinkle detection using hessian line tracking. IEEE Access, 3, 1079-1088.
Nguyen, V. (2018). Smartphone Photography : the Use of Smartphone Camera in Smartphone Camera in 2018. Turku University of Applied Sciences.
Osman, O. F., Elbashir, R. M. I., Abbass, I. E., Kendrick, C., Goyal, M., & Yap, M. H. (2017). Automated assessment of facial wrinkling: A case study on the effect of smoking. 2017 IEEE International Conference on Systems, Man and Cybernetics, SMC 2017, 2017-Janua, 1081-1086.
Rothe, R., Timofte, R., & Van Gool, L. (2018). Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks. International Journal of Computer Vision, 126(2-4), 144-157.
Suprijanto, Ayu, D., Nadhira, V., & Darijanto, S. T. (2009). Development of image processing for digital dermatoscopy. International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering 2009, ICICI-BME 2009.
Tchvialevaa, L., Zenga, H., Markhvidaa, I., McLeana, D. I., Luia, H., & Leea, T. K. (2010). Skin roughness assessment. New Developments in Biomedical Engineering, D. Campolo Ed., InTech, Vukovar, Croatia, 341-358.
Voigt, H., & Classen, R. (2002). Computer vision and digital imaging technology in melanoma detection. Seminars in Oncology, 29(4), 308-327.
Xie, W., Shen, L., & Jiang, J. (2017). A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis. IEEE Transactions on Multimedia, 19(2), 279-292.
Zhou, Z. & Miles, K. (2007). Automatic age estimation based on facial aging. Transactions on Pattern Analysis and Machine Intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2234-2240.
Zhu, X., He, X., Wang, P., He, Q., Gao, D., Cheng, J., & Wu, B. (2016). A method of localization and segmentation of intervertebral discs in spine MRI based on Gabor filter bank. BioMedical Engineering Online, 15(1).
Mr. Elliot Naylor
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom
Dr. Marius Miknis
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom
Dr. Ross Davies
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom