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Image Alignment in Pose Variations of Human Faces by Using Corner Detection Method and Its Application for PIFR System

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Abstract

Major challenge faced by the recent face recognition techniques treat with pose variation during matching. When comparing different person images, the change in facial image caused by motion in the image or due to because of rotation in image is very considerable. Research into Pose Invariant Face Recognition is still an open area in front of developers today. In this paper, we concentrated on PIFR techniques and combined them with other algorithms to achieve better results. Here we are using the Harris Corner Detection model. Image alignment and Image tagging also used to get front face images. We also went into more detail about PIFR and its interrelated operations for future implementation. By generalization different tricks to handle the pose on face images and minimize the pose variation evaluating performance of the system, We are also going to calculate the Euler angle and their position change, and fixing the pose variation based on it for future research,' said the researchers.

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References

  1. Ariz, M., Bengoechea, J. J., Villanueva, A., & Cabeza, R. (2016). A novel 2D/3D database with automatic face annotation for head tracking and pose estimation. Computer Vision and Image Understanding, 148, 201–210.

    Article  Google Scholar 

  2. Lokku, G. K., Harinatha Reddy, G., & Giri Prasad, M. N. (2019). Automatic face recognition for various expressions and facial details. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(9S3), 264–268.

    Article  Google Scholar 

  3. Banerjee, D., & Yu, K. (2018). Robotic arm-based face recognition software test automation. IEEE Access, 6, 37858–37868. https://doi.org/10.1109/ACCESS.2018.2854754

    Article  Google Scholar 

  4. Yang, J., Frangi, A. F., Yang, J. Y., Zhang, D., & Jin, Z. (2005). KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2), 230–244. https://doi.org/10.1109/TPAMI.2005.33

    Article  Google Scholar 

  5. Arashloo, S. R., Kittler, J., & Christmas, W. J. (2011). Pose-invariant face recognition by matching on multi-resolution MRFS linked by super coupling transform. Computer Vision and Image Understanding, 115(7), 1073–1083.

    Article  Google Scholar 

  6. Beymer, D., & Poggio, T. (1995). Face recognition from one example view. In Proceedings of IEEE International Conference on Computer Vision, Cambridge, MA, USA, pp. 500–507. https://doi.org/10.1109/ICCV.1995.466898

  7. Lanitis, A., Taylor, C. J., & Cootes, T. F. (1997). Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 743–756. https://doi.org/10.1109/34.598231

    Article  Google Scholar 

  8. Gao, Y., & Leung, M. K. H. (2002). Face recognition using line edge map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6), 764–779. https://doi.org/10.1109/TPAMI.2002.1008383

    Article  Google Scholar 

  9. Ionita, M. C., Corcoran, P., & Buzuloiu, V. (2009). On color texture normalization for active appearance models. IEEE Transactions on Image Processing, 18(6), 1372–1378. https://doi.org/10.1109/TIP.2009.2017163

    Article  MathSciNet  MATH  Google Scholar 

  10. Xiang, C., Fan, X. A., & Lee, T. H. (2006). Face recognition using recursive Fisher linear discriminant. IEEE Transactions on Image Processing, 15(8), 2097–2105. https://doi.org/10.1109/TIP.2006.875225

    Article  Google Scholar 

  11. Sameem, M.S.I., Qasim, T., & Bakhat, K. (2016). Real time recognition of human faces. In 2016 International Conference on Open Source Systems and Technologies (ICOSST), Lahore, pp. 62–65. https://doi.org/10.1109/ICOSST.2016.7838578

  12. Pan, H., Zhang, Y., Li, C., & Wang, H. (2014). An adaptive harris corner detection algorithm for image mosaic. In Pattern Recognition. CCPR 2014. Communications in Computer and Information Science Springer, Berlin, Heidelberg, vol. 484, pp. 53–62. https://doi.org/10.1007/978-3-662-45643-9_6

  13. Li, D., Zhou, H., & Lam, K. M. (2015). High-resolution face verification using pore-scale facial features. IEEE Transactions on Image Processing, 24(8), 2317–2327.

    Article  MathSciNet  Google Scholar 

  14. An, Z., Deng, W., Hu, J., Zhong, Y., & Zhao, Y. (2019). APA: Adaptive pose alignment for pose-invariant face recognition. IEEE Access, 7, 14653–14670. https://doi.org/10.1109/ACCESS.2019.2894162

    Article  Google Scholar 

  15. Dahm, N., & Gao, Y. (2010). A novel pose invariant face recognition approach using a 2D–3D searching strategy. In 2010 20th International Conference on Pattern Recognition, Istanbul, pp. 3967–3970. https://doi.org/10.1109/ICPR.2010.965

  16. Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720.

    Article  Google Scholar 

  17. Channoufi, I., Bourouis, S., Hamrouni, K., & Bouguila, N. (2016). Deformable models based object tracking: Challenges and current researches. In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, pp. 35–40.

  18. Gross, R., Yang, J., & Waibel, A. (2000) Growing Gaussian mixture models for pose invariant face recognition. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, Barcelona, Spain, vol. 1, pp. 1088–1091.

  19. Wang, D., Hoi, S.C.H., & He, Y. (2011). An effective approach to pose invariant 3D face recognition. In Computer Science Springer, Berlin, Heidelberg International Conference on Multimedia Modeling MMM 2011: Advances in Multimedia Modeling, vol. 6523, pp. 217–228. https://doi.org/10.1007/978-3-642-17832-0_21

  20. Jain, A. K., Klare, B., & Park, U. (2012). Face matching and retrieval in forensics applications. IEEE Multimedia, 19(1), 20–20. https://doi.org/10.1109/MMUL.2012.4

    Article  Google Scholar 

  21. Chu, D., Liao, L., Ng, M. K., & Wang, X. (2015). Incremental linear discriminant analysis: A fast algorithm and comparisons. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2716–2735.

    Article  MathSciNet  Google Scholar 

  22. Lee, H., Kwon, H., Robinson, R.M., & Nothwang, W.D. (2016). DTM: Deformable template matching. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, pp. 1966–1970.

  23. Ratyal, N. I., Taj, I. A., Bajwa, U. I., & Sajid, M. (2015). 3D face recognition based on pose and expression invariant alignment. Computers and Electrical Engineering, 46, 241–255.

    Article  Google Scholar 

  24. Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2008). Multi-PIE. In 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, Amsterdam, pp. 1–8. https://doi.org/10.1109/AFGR.2008.4813399

  25. Xiao, X., & Zhou, Y. (2019). Two-dimensional quaternion PCA and sparse PCA. IEEE Transactions on Neural Networks and Learning Systems, 30(7), 2028–2042. https://doi.org/10.1109/TNNLS.2018.2872541

    Article  Google Scholar 

  26. Peng, X., Yu, X., Sohn, K., Metaxas, D.N., & Chandraker, M. (2017). Reconstruction-based disentanglement for pose-invariant face recognition. In 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 1632–1641.

  27. Chai, X., Shan, S., Chen, X., & Gao, W. (2007). Locally linear regression for pose-invariant face recognition. IEEE Transactions on Image Processing, 16(7), 1716–1725. https://doi.org/10.1109/TIP.2007.899195

    Article  MathSciNet  Google Scholar 

  28. Sharma, P., Yadav, R. N., & Arya, K. V. (2014). Pose-invariant face recognition using curvelet neural network. IET Biometrics, 3(3), 128–138. https://doi.org/10.1049/iet-bmt.2013.0019

    Article  Google Scholar 

  29. Blanz, V., & Vetter, T. (2003). Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1063–1074. https://doi.org/10.1109/TPAMI.2003.1227983

    Article  Google Scholar 

  30. Xiao, X., & Li, L. (2008). Face recognition based on the probability support vector machines. In 2008 International Conference on Computer Science and Software Engineering, Hubei, pp. 907–910. https://doi.org/10.1109/CSSE.2008.869

  31. Timotius, I.K., Linasari, T.C., Setyawan, I., & Febrianto, A.A. (2011). Face recognition using support vector machines and generalized discriminant analysis. In 2011 6th International Conference on Telecommunication Systems, Services, and Applications (TSSA), Bali, pp. 8–10. https://doi.org/10.1109/TSSA.2011.6095397

  32. Li, Y. (2009). A face recognition system using support vector machines and elastic graph matching. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, pp. 3–6. https://doi.org/10.1109/AICI.2009.149

  33. Yu, J., Rui, Y., Tang, Y. Y., & Tao, D. (2014). High-order distance-based multiview stochastic learning in image classification. IEEE Transactions on Cybernetics, 44(12), 2431–2442. https://doi.org/10.1109/TCYB.2014.2307862

    Article  Google Scholar 

  34. Maugey, T., Ortega, A., & Frossard, P. (2015). Graph-based representation for multiview image geometry. IEEE Transactions on Image Processing, 24(5), 1573–1586. https://doi.org/10.1109/TIP.2015.2400817

    Article  MathSciNet  MATH  Google Scholar 

  35. Okada, K., & von der Malsburg, C. (2002) Pose-invariant face recognition with parametric linear subspaces. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA, pp. 71–76.

  36. Baydoun, M., & Al-Alaoui, M. A. (2014). Enhancing stereo matching with classification. IEEE Access, 2, 485–499.

    Article  Google Scholar 

  37. Asthana, A., Jones, M.J., Marks, T.K., Tieu, K.H., & Goecke, R. (2011). Pose normalization via learned 2d warping for fully automatic face recognition. In Proceedings of British Machine and Vision Conference, pp. 1–11. https://doi.org/10.5244/C.25.127

  38. Chehata, R.C.G., Mikhael, W.B., & Abdelwahab, M.M. (2013). Transform domain two dimensional and diagonal modular principal component analysis for facial recognition employing different windowing techniques. In 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), Columbus, OH, pp. 1104–1107. https://doi.org/10.1109/MWSCAS.2013.6674845

  39. Lei, L., Kim, S., Park, W., Kim, D., & Ko, S. (2014). Eigen directional bit-planes for robust face recognition. IEEE Transactions on Consumer Electronics, 60(4), 702–709. https://doi.org/10.1109/TCE.2014.7027346

    Article  Google Scholar 

  40. Arashloo, S. R., & Kittler, J. (2014). Fast pose invariant face recognition using super coupled multiresolution Markov random fields on a GPU. Pattern Recognition Letter, 48, 49–59. https://doi.org/10.1016/j.patrec.2014.05.017

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Uttrakhand Technical University, Dehradun, 248007, India

    Deepika Dubey

  2. Department of Electronic and Communication Engineering, Rajkiya Engineering College, Sonbhadra, 231206, India

    Geetam Singh Tomar

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  1. Deepika Dubey
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  2. Geetam Singh Tomar
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Correspondence to Geetam Singh Tomar.

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Dubey, D., Tomar, G.S. Image Alignment in Pose Variations of Human Faces by Using Corner Detection Method and Its Application for PIFR System. Wireless Pers Commun 124, 147–162 (2022). https://doi.org/10.1007/s11277-021-09330-1

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