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Proceedings of the 2022 International Conference on Research in Management & Technovation

Annals of Computer Science and Information Systems, Volume 34

Face Recognition Technology Using the Fusion of Local Descriptors

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DOI: http://dx.doi.org/10.15439/2022M6308

Citation: Proceedings of the 2022 International Conference on Research in Management & Technovation, Viet Ha Hoang, Vijender Kumar Solanki, Nguyen Thi Hong Nga, Shivani Agarwal (eds). ACSIS, Vol. 34, pages 227231 ()

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Abstract. Local phase quantization (LPQ) descriptor, first introduced by Ojansivu and Heikkila (2008), has successfully been applied in face recognition systems. In this paper, we combine local intensity area descriptor (LIAD), which was first introduced by Tran (2017), with LPQ descriptor to develop robust face recognition systems using LPQ descriptor. Face images were first encoded by LIAD as a noise and dimensionality reduction step. After that, the resulting images were presented through LPQ as a feature extraction step. A nearest neighbor method with chi-square measure is used in classification. Two famous datasets (the ORL Database of Faces and FERET) were used in experiments. The results confirmed that our proposed approach reached mean recognition accuracies that are 0.17\% ÷ 7.7\% better compared to five conventional descriptors (LBP, LDP, LDN, LTP, and LPQ).


  1. M. O. Oloyede, G. P. Hancke, and H. C. Myburgh, "A review on face recognition systems: recent approaches and challenges," Multimedia Tools and Applications, vol. 79, no. 37, pp. 27891-27922, 2020.
  2. C. K. Tran, C. D. Tseng, L. Chang, and T. F. Lee, "Face recognition under varying lighting conditions: improving the recognition accuracy for local descriptors based on weber-face followed by difference of Gaussians," Journal of the Chinese Institute of Engineers, vol. 42, no. 7, pp. 593-601, 2019.
  3. J. Sun, Y. Lv, C. Tang, H. Sima, and X. Wu, "Face Recognition Based on Local Gradient Number Pattern and Fuzzy Convex-Concave Partition," IEEE Access, vol. 8, pp. 35777-35791, 2020.
  4. X. Wei, H. Wang, B. Scotney, and H. Wan, "Selective multi-descriptor fusion for face identification," International Journal of Machine Learning and Cybernetics, vol. 10, no. 12, pp. 3417-3429, 2019.
  5. F. Liu, Z. Tang, and J. Tang, "WLBP: Weber local binary pattern for local image description," Neurocomputing, vol. 120, pp. 325-335, 2013.
  6. T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on feature distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.
  7. T. Xiaoyang and B. Triggs, "Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions," Image Processing, IEEE Transactions on, vol. 19, no. 6, pp. 1635-1650, 2010.
  8. T. Jabid, M. H. Kabir, and C. Oksam, "Facial expression recognition using Local Directional Pattern (LDP)," in Image Processing (ICIP), 2010 17th IEEE International Conference on, 2010, pp. 1605-1608.
  9. C. K. Tran, T. F. Lee, and P. J. Chao, "Improving face recognition performance using similarity feature-based selection and classification algorithm," Journal of Information Hiding and Multimedia Signal Processing, vol. 6, no. 1, 2015.
  10. T. Ahonen, E. Rahtu, V. Ojansivu, and J. Heikkilä, "Recognition of Blurred Faces Using Local Phase Quantization," in International Conference on Pattern Recognition, 2008, pp. 1-4.
  11. V. Ojansivu and J. Heikkilä, "Blur Insensitive Texture Classification Using Local Phase Quantization," in Image and Signal Processing, vol. 5099, A. Elmoataz, O. Lezoray, F. Nouboud, and D. Mammass, Eds. (Lecture Notes in Computer Science: Springer Berlin Heidelberg, 2008, pp. 236-243.
  12. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int. J. Comput. Vision, vol. 60, no. 2, pp. 91-110, 2004.
  13. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.
  14. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 886-893 vol. 1.
  15. C. K. Tran et al., "Local intensity area descriptor for facial recognition in ideal and noise conditions," Journal of Electronic Imaging, vol. 26, no. 2, pp. 023011-1 - 023011-10, 2017.
  16. A. T. L. Cambridge. The Database of Faces [Online]. Available: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
  17. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, 2000.
  18. E. Kreyszig, Advanced Engineering Mathematics, 10 ed. John Wiley & Sons, 2010, p. 1264.
  19. F. Jafari and H. Rashidy Kanan, "Disguised Face Recognition by Using Local Phase Quantization and Singular Value Decomposition," (in en), Journal of Computer & Robotics, vol. 9, no. 1, pp. 51-60, 2016.
  20. J. Jiao and Z. Deng, "Deep combining of local phase quantization and histogram of oriented gradients for indoor positioning based on smartphone camera," International Journal of Distributed Sensor Networks, vol. 13, no. 1, 2017.
  21. L. Nanni, S. Brahnam, and A. Lumini, "Local phase quantization descriptor for improving shape retrieval/classification," Pattern Recognition Letters, vol. 33, no. 16, pp. 2254-2260, 2012.
  22. C. K. Tran, T. H. Ngo, C. N. Nguyen, and L. A. Nguyen, "SVM-Based Face Recognition through Difference of Gaussians and Local Phase Quantization," International Journal of Computer Theory and Engineering, vol. 13, no. 1, pp. 1-8, 2021.