Newton’s Law of Gravitational Force (NLGF) based Machine Learning Technique for Uneven Illuminated Face Detection

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Published: Jul 13, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i7s.7021
Keywords:
NLGF, ENLGF, Gravitational Force, Face recognition, Gamma-correction

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M. Shalima Sulthana
Research Scholar, Department of Computer Science and Engineering, YSR Engineering College of YVU, Yogivemana University- Kadapa
C. Naga Raju
Professor, Department of Computer Science and Engineering, YSR Engineering College of YVU, Yogivemana University- Kadapa

Abstract

A photo gallery is crucial for organizing your photos, presenting them in beautiful categories, and doing sophisticated memory searches. The photo gallery is portrayed in a vocabulary of nonlinear similarities to the prototype face image collection. One of the difficult research ideas for machine learning technologies is the maintenance of a photo gallery using facial recognition. Based on changes in the faces' appearance, faces are identified. This research proposes novel machine learning algorithms to recognize faces by characterizing the majority of discriminating local characteristics, which maximizes the dissimilarity between face photos of different persons and reduces the dissimilarity between features between face images of the same person. This method relies on Newton's third law of gravitational force to determine the relationship between pixels to extract the features of noisy accurately and efficiently, unevenly illuminated, and rotationally invariant face images.

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How to Cite
Sulthana, M. S., & Raju, C. N. (2023). Newton’s Law of Gravitational Force (NLGF) based Machine Learning Technique for Uneven Illuminated Face Detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 447–464. https://doi.org/10.17762/ijritcc.v11i7s.7021
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Vol. 11 No. 7s (2023)
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Articles

References

B. Moghaddam, T. Jebara, and A. Petland, “Bayesian face recognition,” J. Pattern Recognit., vol. 33, no. 11, pp. 1771–1782, 2000.

G. Guo, S. Li, and K. Chan, “Face recognition by support vector machines,” in Proc. IEEE International Conference on Automatic Face and Gesture Recognition (AFGR’00), 2000, pp. 196–201

M. Turk and A. Pentland, “Eigen faces for recognition,” J. Cognitive Neursci., vol. 3, no. 1, pp. 71–86, 1991.

R.C.Gonzalez and R. E. Woods, Digital Image Processing. Pearson Prentice Hall, 3rd Edition.

M. Savvides and V. Kumar, “Illumination normalization using logarithm transforms for face authentication,” in Proc. IAPR AVBPA, 2003, pp.549–556.

S.M.Pizerand E. P. Amburn, “Adaptive histogram equalization and its variations,” J. Comput. Vis. Graph. Image Process., vol. 39, no. 3, pp. 355–368, 1987.

V. Blanz and T. Vetter, “Face recognition based on fitting a 3d morphable model,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp.1063–1073, 2003

L. Zheng, Y. Yang, and Q. Tian, “Sift meets cnn: A decade survey of instance retrieval,” arXiv reprint arXiv:1608.01807v2, 2015.

Fan, B., Kong, Q., Trzcinski, T., Wang, Z., Pan, C., &Fua, P. (2014). Receptive fields selection for binary feature description. IEEE Transactions on Image Processing, 26(6), 2583–2595.

Trzcinski, T., Christoudias, M., Lepetit, V., &Fua, P. (2012). Learning image descriptors with the boosting-trick. In Advances in neural information processing systems (pp. 269–277).

Balntas, V., Tang, L., &Mikolajczyk, K. (2015). Bold - binary online learned descriptor for efficient image matching. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2367–2375)

D. Lowe, “Distinctive image features from scale invariant key points,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, 2004.

H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-up robust features (surf),” J. omput. Vis. Img. Understanding, vol. 110, no. 3,pp. 346–359, 2008.

E. Tola, V. Lepetit, and P. Fua, “Daisy: An efficient dense descriptor applied to wide-baseline stereo,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 5, pp. 815–830, 2010.

A. Iscen, G. Tolias, P. Gosselin, and H. Jeguo, “A comparison of dense region detectors for image search and fine-grained classification,” IEEE Trans. Image Process., vol. 24, no. 8, pp. 2369–2381, 2015.

C.Nagaraju, D.Sharadamani, C.Maheswari and D.Vishnu Vardhan Evaluation of LBP-Based Facial emotions recognition techniques to make consistent decisions Int.J.Pattern Recognition AND Artificial Intelligence 2015.

D.G. Lowe, “Distinctive Image Features from Scale Invariant Keypoints,” Int’l J. Computer Vision, vol. 20, no. 2, pp. 91-110, 2004

C. Strecha, T. Tuytelaars, and L. Van Gool, “Dense Matching of Multiple Wide Baseline Views,” Proc. Int’l Conf. Computer Vision,2003.

Lin, K., Lu, J., Chen, C. S., Zhou, J., & Sun, M. T. (2018). Unsupervised deep learning of compact binary descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6), 1501–514.

J Ma, X Jiang , A Fan, J Jiang, J Yan 2021 .image matching from handcrafted to deep features: A survey , International Journal of Computer Visionvolume 129, pages23–79 (2021).

Dr. B. Maruthi Shankar. (2019). Neural Network Based Hurdle Avoidance System for Smart Vehicles. International Journal of New Practices in Management and Engineering, 8(04), 01 - 07. https://doi.org/10.17762/ijnpme.v8i04.79

Tuytelaars, T., Mikolajczyk, K., et al. (2008). Local invariant feature detectors: A survey. Foundations and Trends® in Computer Graphics and Vision, 3(3), 177–280.

Li, Y., Wang, S., Tian, Q., & Ding, X. (2015). A survey of recent advances in visual feature detection. Neurocomputing, 149, 736–751.

Rosten, E., Porter, R., & Drummond, T. (2010). Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), 105–119.

Moravec, H. P. (1977). Techniques towards automatic visual obstacle avoidance.

Smith, S. M., & Brady, J. M. (1997). Susan: A new approach to low level image processing. International Journal of Computer Vision, 23(1), 45–78.

Belongie, S., Malik, J., &Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4, 509–522.

Richardson, A., & Olson, E. (2013). Learning convolutional filters for interest point detection. In Proceedings of the IEEE international conference on robotics and automation, pp. 631–637.

Parjane, V. A. ., Arjariya, T. ., & Gangwar, M. . (2023). Corrosion Detection and Prediction for Underwater pipelines using IoT and Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 293 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2626

Strecha, C., Lindner, A., Ali, K., &Fua, P. (2009). Training for task specific keypoint detection. In Joint pattern recognition symposium, Springer, pp. 151–160.

Trajkovi?, M., & Hedley, M. (1998). Fast corner detection. Image and Vision Computing, 16(2), 75–87.

Zhang, X., Yu, F. X., Karaman, S., & Chang, S. F. (2017b). Learning discriminative and transformation covariant local feature detectors. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6818–6826.

Prateek Singhal, Prabhat Kumar Srivastava., Arvind Kumar Tiwari, Ratnesh Kumar Shukla “A Survey: Approaches to Facial Detection and Recognition with Machine Learning Techniques”. Proceedings of Second Doctoral Symposium on Computational Intelligence 2021.

Mwangi, J., Cohen, D., Silva, C., Min-ji, K., & Suzuki, H. Feature Extraction Techniques for Natural Language Processing Tasks. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/137

Bhanushree K. J., Meenavathi M. B “Feature Based Face Recognition using Machine Learning Techniques”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-6, March 2020