Journal of Control & Instrumentation

Journal of Control & Instrumentation Review Article

Face And Spoofing Detection Via Genetic Algorithm-Based Feature Selection with MTCNN

  1. Karan R Talwalkar Department of Electronics & Communication Engineering, Shri Vile Parle Kelavani Mandal Narsee Monjee Institute of Management Studies, Thane
  2. Kshitj Navale Department of Electronics & Communication Engineering, Shri Vile Parle Kelavani Mandal Narsee Monjee Institute of Management Studies, Thane
  3. Aditya Ashok Department of Electronics & Communication Engineering, Shri Vile Parle Kelavani Mandal Narsee Monjee Institute of Management Studies, Thane

Abstract

Face detection and liveness detection in various environments such as different lighting effects, occlusion, complex backgrounds, and different poses and angles play an important role in facial recognition or detection purposes. In this research paper, propose an improved algorithm for face detection and liveness, spoofing detection via genetic algorithm for feature selection, and mtcc detecting the edge of the facial image by Canny filter and spotting the face from the image and whether the image is spoofed or not. These experimental results used various types of complex image datasets and generated datasets. In this algorithm, edge detection is done by using the canny detector. We tried to solve the problem of low-contrast images. In the preprocessing stage, we used an image-denoising algorithm for removing noise from the image. The detection rate has reached 100% Real-time face detection using MTCNN.

Keywords

  • Canny Detector
  • Expression Detection
  • Face detection
  • Genetic Algorithm
  • fast NÍ Means Denoising Colored MTCNN

References (23)

  1. Sandeep Kumar, Sukhwinder Singh, and Jagdish Kumar, “A Study on Face Recognition Techniques with Age and Gender Classification”, In IEEE International Conference on Computing, Communication and Automation (ICCCA), 5th -6 th May 2017.
  2. Kumar S, Singh S, Kumar J. A comparative study on face spoofing attacks. 2017 International Conference on Computing, Communication and Automation (ICCCA). 2017:1104-1108. doi:10.1109/ccaa.2017.8229961
  3. Sandeep Kumar, Deepika, and Munish Kumar, “An Improved Face Detection Technique for a Long Distance and Near-Infrared Images”, The Advances in Computational Sciences and Technology. 2017;
  4. Hatem, Hiyam, Zou Beiji, and Raed Ma "A Survey of Feature Base Methods for Human Face Detection." International Journal of Control and Automation. 2015; 8(5): 61-78. https://article.nadiapub.com/IJCA/vol8_no5/7.pdf
  5. Ghimire, Deepak, and Joonwhoan Lee. "A robust face detection method based on skin color and edges" Journal of Information Processing 2013; 9(1): 141-156.
  6. Peng S, Ser W, Chen B, Sun L, Lin Z. Robust constrained adaptive filtering under minimum error entropy criterion. IEEE Transactions on Circuits and Systems II: Express Briefs. 2018 Jan 3;65(8):1119-23.
  7. Srinivas M, Patnaik LM. Genetic algorithms: a survey. Computer. 1994;27(6):17-26. doi:10.1109/2.294849
  8. Sandeep Kumar, Sukhwinder Singh, and Jagdish Kumar,” Automatic Face detection Using Genetic Algorithm for various challenges”. International Journal of Scientific Research and Modern Education. 2017; 2(1): 197-203.
  9. Sukhija P, Behal S, Singh P. Face Recognition System Using Genetic Algorithm. Procedia Computer Science. 2016;85:410-417. doi:10.1016/j.procs.2016.05.183
  10. Pratap and N. Kumar, “Face Recognition using Genetic Algorithm and Neural Networks,” International Journal of Computer Applications. 2012; 55(4): 975–8887.
  11. Musikhin AG, Yu Burenin S. Face recognition using multitasking cascading convolutional networks. IOP Conference Series: Materials Science and Engineering. 2021;1155(1):012057. doi:10.1088/1757-899x/1155/1/012057
  12. G. C, K. H. S, S. Shirahatti, and S. R. Bangari, “Face Recognition System for Real Time Applications using SVM Combined with FACENET and MTCNN,” International Journal of Electrical Engineering and Technology (IJEET), 12(6): 328–335, 2021, doi:10.34218/IJEET.12.6.2021.031.
  13. Khan SS, Sengupta D, Ghosh A, Chaudhuri A. MTCNN++: A CNN-based face detection algorithm inspired by MTCNN. The Visual Computer. 2023;40(2):899-917. doi:10.1007/s00371-023-02822-0
  14. Mahmud F, Haque ME, Zuhori ST, Pal B. Human face recognition using PCA based Genetic Algorithm. 2014 International Conference on Electrical Engineering and Information & Communication Technology. 2014:1-5. doi:10.1109/iceeict.2014.6919046
  15. Zhi H, Liu S. Face recognition based on genetic algorithm. Journal of Visual Communication and Image Representation. 2019;58:495-502. doi:10.1016/j.jvcir.2018.12.012
  16. Chingovska, A. Anjos and S. Marcel, "On the effectiveness of local binary patterns in face anti-spoofing," 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2012, pp. 1-7.
  17. Komulainen J, Hadid A, Pietikainen M. Context based face anti-spoofing. 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS). 2013:1-8. doi:10.1109/btas.2013.6712690
  18. Maatta J, Hadid A, Pietikainen M. Face spoofing detection from single images using micro-texture analysis. 2011 International Joint Conference on Biometrics (IJCB). 2011:1-7. doi:10.1109/ijcb.2011.6117510
  19. Galbally J, Marcel S, Fierrez J. Biometric Antispoofing Methods: A Survey in Face Recognition. IEEE Access. 2014;2:1530-1552. doi:10.1109/access.2014.2381273
  20. Boulkenafet Z, Komulainen J, Hadid A. Face Anti-Spoofing using Speeded-Up Robust Features and Fisher Vector Encoding. IEEE Signal Processing Letters. 2016:1-1. doi:10.1109/lsp.2016.2630740
  21. Canny J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986;PAMI-8(6):679-698. doi:10.1109/tpami.1986.4767851
  22. Kang WX, Yang QQ, Liang RP. The comparative research on image segmentation algorithms. In2009 First international workshop on education technology and computer science 2009 Mar 7 (Vol. 2, pp. 703-707). IEEE.
  23. Verma OP, Parihar AS. An optimal fuzzy system for edge detection in color images using bacterial foraging algorithm. IEEE Transactions on Fuzzy systems. 2016 Apr 6;25(1):114-27.
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