An Analytical Approach for Reconstruction of Cosmetic Surgery Images using EUCLBP and SIFT
|International Journal of Electrical and Electronics Engineering|
|© 2022 by SSRG - IJEEE Journal|
|Volume 9 Issue 8|
|Year of Publication : 2022|
|Authors : Shiji S K, Dr. S.H Krishnaveni|
How to Cite?
Shiji S K, Dr. S.H Krishnaveni, "An Analytical Approach for Reconstruction of Cosmetic Surgery Images using EUCLBP and SIFT," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 8, pp. 60-71, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I8P107
Plastic surgery is a surgical procedure with outcomes often opposite to facial ageing. Face recognition algorithms find it difficult to anticipate such non-uniform face transformations because any changes brought on by plastic surgery procedures happen quickly. In this paper, the performance of face reconstruction is compared with that of two commonly used feature extractors, Extended Uniform Circular Local Binary Pattern (EUCLBP) and Scale Invariant Feature Transform (SIFT). An open plastic surgery facial dataset of 1800 before and after operation picture samples for 900 human face photographs has been used to test all algorithms. For each human subject, two front-facing image samples with appropriate luminance and neutral motions are gathered; the first is taken before, and the second is obtained after surgery. The proposed method is validated using parameters like accuracy, sensitivity, specificity, F-Score, G-mean and Precision. These results consistently show superior performance and high identification accuracy of 97% in combination with using both feature extractors EUCLBP+SIFT rather than using any one [EUCLBP or SIFT]alone.
Face Reconstruction, plastic surgery, granular computing, Extended Uniform Circular Local Binary Pattern, Scale Invariant Feature Transform.
 Himanshu S. Bhatt, Student Member, IEEE, Samarth Bharadwaj, Student Member, IEEE, Richa Singh, Member, IEEE, and MayankVatsa, Member, "Recognizing Surgically Altered Face Images Using Multi-objective Evolutionary Algorithm" IEEE 2013 IEEE transaction vol.8 , 2013.
 Shashua, A. and Riklin-Raviv, T, “ The Quotient Image: Class-Based Re Rendering and Recognition with Varying Illuminations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, no.2, pp.129-139, 2001.
 Wang, H. Li, S. Z. and Wang, Y, “ Generalized Quotient Image, ” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. June 27-July 2, 2004. Washington, DC, USA: IEEE. 2004. 498505.
 Lee, J. Moghaddam, B. Pfister, H. and Machiraju, R, “ A Bilinear Illumination Model for Robust Face Recognition,” Proceedings of the Tenth IEEE International Conference on Computer Vision. October 17-21, Beijing: IEEE, pp.1177-1184, 2005.
 Zhao, J., Su, Y., Wang, D. and Luo, S, “ Illumination Ratio Image: Synthesizing and Recognition with Varying Illuminations,” Pattern Recognition Letters, vol.24, no.15, pp.2703-2710, 2003.
 Han, H., Shan, S., Qing, L., Chen, X. and Gao, W, “Lighting Aware Pre-processing for Face Recognition Across Varying Illumination,” Proceedings of the 11th European Conference on Computer Vision Part II. September 5-11, 2010. Heraklion, Crete, Greece: Springer Berlin Heidelberg, pp.308-321, 2010.
 Zhang, L. and Samaras, D, “Face Recognition from a Single Training Image under Arbitrary Unknown Lighting Using Spherical Harmonics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.3, pp.211 351-363, 2006.
 Wang, Y., Liu, Z., Hua, G., Wen, Z., Zhang, Z. and Samaras, D, “ Face ReLighting from a Single Image under Harsh Lighting Conditions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 17-22, Minneapolis, MN, USA: IEEE, pp.1-8, 2007.
 Lowe, D. G, ”Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol.60, no.2, pp.91-110, 2004.
 Moghaddam, B. and Pentland, A, “Probabilistic Visual Learning for Object Detection,” Proceedings of the Fifth International Conference on Computer Vision. June 20-23, 1995. Cambridge, MA, USA: IEEE, pp.786-793, 1995.
 Anwarul, S., &Dahiya, S, “ A comprehensive review of face recognition methods and factors affecting facial recognition accuracy,” Proceedings of ICRIC, pp.495-514, 2019.
 Kimaya S. Meshram and Ajay M. Agarkar, "Content-Based Image Retrieval Systems using SIFT: A Survey," SSRG International Journal of Electronics and Communication Engineering, vol.2, no. 10, pp.16-22, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I10P105
 Moreano, J. A. C., & Palomino, N. B. L. S, “ Global Facial Recognition Using Gabor Wavelet, Support Vector Machines and 3D Face Models,” Journal of Advances in Information Technology, vol.11, no.3, 2020.
 Lin, C. H., Wang, Z. H., & Jong, G. J, “A de-identification face recognition using extracted thermal features based on deep learning,” IEEE Sensors Journal, vol.20, no.16, pp.9510-9517, 2020.
 Vinita Bhandiwad, "A Review Paper on 2D to 3D Face Recognition Technology," SSRG International Journal of VLSI & Signal Processing, vol.2, no. 2, pp.10-13, 2015. Crossref, https://doi.org/10.14445/23942584/IJVSP-V2I2P103
 Kefalea, E, “Object Localization and Recognition for a Grasping Robot,” Proceedings of the 24th Annual Conference of the IEEE on Industrial Electronics Society, August 31-September 4, 1998. Aachen, Germany: IEEE, pp.2057-2062, 1998.
 Yuan, H., Ma, H. and Huang, X, “Edge-based Synthetic Discriminant Function for Distortion Invariant Object Recognition,” Proceedings of the 15th IEEE International Conference on Image Processing. October 12-15, 2008. San Diego, CA, USA: IEEE, pp.2352-2355, 2008.
 Chalechale, A., Mertins, A. and Naghdy, G, “ Edge Image Description using Angular Radial Partitioning,” IEEE Proceedings on Vision, Image and Signal Processing, vol.151, no.2, pp.93-101, 2004.
 Gao, Y. and Leung, M. K, “Face Recognition Using Line Edge Map, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.6, pp.764-779, 2002.
 Gao, Y. and Qi, Y, “Robust Visual Similarity Retrieval in Single Model Face Databases,” Journal of Pattern Recognition, vol.38, no.7, pp.1009-1020, 2005.
 Suzuki, Y. and Shibata, T, “An Edge-Based Face Detection Algorithm RobustAgainst Illumination, Focus, and Scale Variations,” Proceedings of the 12th European Signal Processing Conference. September 6-10, 2004. Vienna, Austria: EURASIP, pp.2279–2282, 2004.
 Zhao-Yi, P., Yan-Hui, Z. and Yu, Z, “ Real-Time Facial Expression Recognition Based on Adaptive Canny Operator Edge Detection,” Proceedings of the Second International Conference on Multimedia and Information Technology, April 24-25, 2010. Kaifeng: IEEE. Pp.154-157, 2010.
 Arandiga, F., Cohen, A., Donat, R. and Matei, B, “Edge Detection Insensitive to Changes of Illumination in the Image, ” Journal of Image and Vision Computing, vol.28, no.4, pp.553-562, 2018.
 Suzuki, Y. and Shibata, T, “Illumination-invariant Face Identification Using Edge-Based Feature Vectors in Pseudo-2D Hidden Markov models,” Proceedings of the 14th European Signal Processing Conference. September 4-8, 2006. Florence, Italy: EURASIP, pp.4-8, 2006.
 Samsung, S, “ Integral Normalized Gradient Image A Novel Illumination Insensitive Representation,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, June 25-25, 2005. San Diego, CA, USA: IEEE, pp.166-166, 2005.
 Arandjelovic O, “Gradient Edge Map Features for Frontal Face Recognition under Extreme Illumination Changes,” Proceedings of the British Machine Vision Conference. September 3-7, 2012. Surrey: BMVA Press, pp.1-11, 2012.
 Zhang, T., Tang, Y., Fang, B., Shang, Z. and Liu, X, “Face Recognition under Varying Illumination using Gradient Faces,” IEEE Transactions on Image Processing, vol.18, no.11, pp.2599-2606, 2012.
 Han, H., Shan, S., Chen, X., Lao, S. and Gao, W, “Separability Oriented Pre-processing for Illumination-Insensitive Face Recognition,” Proceedings of the 12th European Conference on Computer Vision Part VII, October 7-13, 2012. Florence, Italy: Springer Berlin Heidelberg, pp.307-320, 2012.
 Chen, W., Er, M. and Wu, S, “Illumination Compensation and Normalization for Robust Face Recognition using Discrete Cosine Transform in Logarithm Domain,” IEEE Transactions on Systems, Man, and Cybernetics, Part B,vol.36, no.2, pp.458-46, 2006.
 Bouzgarne Itri, Youssfi Mohamed, Bouattane Omar, Qbadou Mohamed, "Composition of Feature Selection Methods And Oversampling Techniques For Banking Fraud Detection With Artificial Intelligence" International Journal of Engineering Trends and Technology, vol.69, no.11, pp.216-226, 2021.
 Wang, H., Li, S. Z., Wang, Y. and Zhang, J, “SelfQuotient Image for face recognition,” Proceedings of the International Conference on Image Processing, October 24-27, 2004. Singapore: IEEE, pp.1397-1400, 2004.
 Nishiyama M., Kozakaya T. and Yamaguchi O, “Illumination Normalization using Quotient Image-Based Techniques,” In: Delac, K., Grgic, M. and Bartlett, M. S. ed. Recent Advances in Face Recognition. Vienna, Austria: I213 The, pp.97-108,2008.
 Gross, R. and Brajovic, V, “An Image Pre-Processing Algorithm for Illumination Invariant Face Recognition,” Proceedings of the 4th International Conference on Audio-and Video-Based Biometric Person Authentication. June 9–11, 2003. Guildford, UK: Springer Berlin Heidelberg, pp.10-18, 2003.
 Tang, Z. and Whitaker, R. T, “Modified Anisotropic Diffusion for Image Smoothing and Enhancement,” Proceedings of the 12th SPIE 4304 on Non-linear Image Processing and Pattern Analysis, 8 May 2001. San Jose, CA: SPIE, pp.318-325, 2001.
 Du, S. and Ward, R, “Wavelet-Based Illumination Normalization for Face Recognition,” Proceedings of the IEEE International Conference on Image Processing, September 11-14, 2005. Genova, Italy: IEEE, pp.954-95, 2005.
 Shwetambari Borade, Dhananjay Kalbande, Kristen Pereira, Rushil Patel, Sudhanshu Kulkarni, "Deep Scattering Convolutional Network for Cosmetic Skin Classification," International Journal of Engineering Trends and Technology, vol.70, no. 7, pp.10-23, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P202.
 Pooja G Nair, Sneha R, "A Review: Facial Recognition Using Machine Learning, " International Journal of Recent Engineering Science, vol.7, no.3, pp.85-89, 2020.
 Jobson, D. J., Rahman, Z. and Woodell, G. A, “A Single-Scale Retinex for Bridging the Gap BetweenColour Images and the Human Observation of Scenes,” IEEE Transactions on Image Processing, vol.6, no.7, pp.965-976.
 Singh, R., Agarwal, A., Singh, M., Nagpal, S., &Vatsa, M, “On The Robustness of Face Recognition Algorithms Against Attacks and Bias,” In Proceedings of the AAAI Conference on Artificial Intelligence , vol.34, no. 09, pp.13583-13589.
 Sammaiah Seelothu, Dr K. Venugopal Rao "An Aggregated Optical Flow Vectors for Micro Expression Recognition Using Spatio-Temporal Binary Pattern Coding," International Journal of Engineering Trends and Technology, vol.69, no.11, pp.236-247, 2021. Crossef, https://doi.org/10.14445/22315381/IJETT-V69I11P230.
 Rathgeb, C., Dogan, D., Stockhardt, F., De Marsico, M., & Busch, C, “Plastic surgery: An obstacle for deep face recognition?” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pp.806-807, 2020.