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An efficient PCA based pose and occlusion invariant face recognition system for video surveillance

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Abstract

In the proposed work introduced a pose-invariant face recognition process for the videos so as todiminish the calculation intricacy of the traditional technique. The intended approach includes four phases, namely (i) preprocessing, (ii) object detection, (iii) feature extraction along with (iv) dimensionality reduction. The initial phase is segmenting the database video clips into the frames where the preprocessing is done using an adaptive median filtering such that to eradicate the noise. The subsequent phase is detecting the preprocessed image through the viola–jones technique. With this technique, the mouth, left eye, face, nose, right eye were identified. After that, the training image attributes like the texture, edge, and wavelet are extorted with the assistance of dissimilar feature extortion approaches. The extorted attributes will then be inputted to the PCA approach. The similar process is replicated for query images. At last, perform the similarity measurement amid the trained database images and query images such that to attain the distinguished image. In addition, the genuine general performance assesses of the projected technique is assessed with that of the traditional ICA and also LDA techniques.

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References

  1. Choi, J.Y., Plataniotis, K.N., Ro, Y.M.: Face feature weighted fusion based on fuzzy membership degree for video face recognition. IEEE Trans. Syst. Man Cybern. Part B 42(4), 1270–1282 (2012)

    Article  Google Scholar 

  2. Tolba, A.S., El-Baz, A.H., El-Harby, A.A.: Face recognition: a literature review. Int. J. Signal Process. 2(2), 88–103 (2006)

    Google Scholar 

  3. Majumdar, A., Ward, R.K.: Pseudo-Fisher face method for single image per person face recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, pp. 989–992 (2008)

  4. Yoganad, V., Celine K.A.: Region growing and modified neural network classifier based face detection technique from video, vol. 10, No. 12, pp. 30231–30248 (2015)

  5. Shermina, J., Vasudevan, V.: Face recognition system with various expression and occlusion based on a novel block matching algorithm and PCA. Int. J. Comput. Appl. 38(11), 27–34 (2012)

    Google Scholar 

  6. Kumar, P., Agarwal, M.M., Nagar, M.S.: A survey on face recognition system—a challenge. Int. J. Adv. Res. Comput. Commun. Eng. 2, 2167–2171 (2013)

    Google Scholar 

  7. Milki, H., Fendri, E., Mohamed, H.: Face recognition through different facial expressions. J. Signal Process. Syst. 81, 433–446 (2015)

    Article  Google Scholar 

  8. Abdel-Kader, R.F., Ramadan, R.M., Rizk, R.Y.: Rotation invariant face recognition based on hybrid LPT/DCT features. Int. J. Electr. Comput. Eng. 3(7), 488–493 (2008)

    Google Scholar 

  9. Suzuki, Y., Shibata, T.: Illumination-invariant face identification using edge-based feature vectors in Pseudo-2d Hidden Markov models. In: Proceedings of the 14th European Signal Processing Conference, pp. 1–5. Florence, Italy (2006)

  10. Li, X., Fukui, K., Zheng, N.: Image-set based face recognition using boosted global and local principal angles. In: Asian Conference on Computer Vision, pp. 323–332. Springer, Berlin (2009)

  11. Li, W.J., Luo, B., Wang, C.J., Zhong, X.P., Chen, Z.Q.: A multiple eigenspaces constructing method and its application to face recognition. Proc. First Int. Conf. Adv. Nat. Comput. 3611, 55–64 (2005)

    Google Scholar 

  12. Singh, A., Tiwari, S., Singh, S.K.: Comparison of face recognition algorithms on dummy faces. Adv. Intell. soft Comput. 166, 211–222 (2012)

    Article  Google Scholar 

  13. Hadid, A., Pietikainen, M.: Combining appearance and motion for face and gender recognition from videos. Pattern Recognit. 42(11), 2818–2827 (2009)

    Article  Google Scholar 

  14. Sarode, J.P., Anuse, A.D.: Face recognition under pose variations. Int. J. Comput. Sci. Inf. Technol. 5(3), 2689–2693 (2014)

    Google Scholar 

  15. Suneetha, J.: A survey on video-based face recognition approaches. Int. J. Appl. Innov. Eng. Manag. 3(2), 208–215 (2014)

    Google Scholar 

  16. Raducanu, B., Dornaika, F.: Pose-invariant face recognition in videos for human-machine interaction, vol. 7584, pp. 566–575. Springer, Berlin (2012)

    Google Scholar 

  17. Zhou, S.K., Chellappa: R.: Image-based face recognition under illumination and pose variations. JOSA A 22(2), 217–229 (2005)

    Article  Google Scholar 

  18. Shermina, J.: Impact of locally linear regression and fisher linear discriminant analysis in pose invariant face recognition. Int. J. Comput. Sci. Netw. Secur. 10(10), 106–110 (2010)

    Google Scholar 

  19. Chen, H.-F., Hong, Z.-W.: Pose and expression invariant face recognition from a single training sample using similarity vector. J. Cybern. Syst. 1(1), 21–26 (2008)

    Google Scholar 

  20. Ng, H.-F.: Pose-invariant face recognition security system. Asian J. Health Inf. Sci. 1(1), 101–111 (2006)

    Google Scholar 

  21. Parmar, Divyarajsinh, Mehta, Brijesh: Face recognition methods and applications. Comput. Technol. Appl. 4, 84–86 (2013)

    Google Scholar 

  22. Srinivasan, A.: A framework for face recognition using adaptive binning and AdAboost techniques. Int. J. Multimed. Appl. 3(1), 76–88 (2011)

    Google Scholar 

  23. Cevikalp, H., Triggs, B.: Face recognition based on image sets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2567–2573. San Francisco, United States (2010)

  24. Wang, C., Wang, Y., Zhang, Z., Wang, Y.: Incremental learning patch-based bag of facial words representation for face recognition in videos. Multimed. Tools Appl. 72(3), 2439–2467 (2013)

    Article  Google Scholar 

  25. Arandjelovic, O., Cipollla, R.: Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant. Pattern Recognit. 46(1), 9–23 (2013)

    Article  Google Scholar 

  26. Sharma, P., Yadav, R., Arya, K.: Pose-invariant face recognition using curvelet neural network. IEEE Trans. Biom. IET 3(3), 128–138 (2014)

    Google Scholar 

  27. Ali, A.M.: A 3D-based pose invariant face recognition at a distance framework. IEEE Trans. Inf. Forensics Secur. 9(12), 2158–2169 (2004)

    Article  Google Scholar 

  28. De-la-Torre, M., Granger, E., Sabourin, R., Gorodnichy, D.O.: An adaptive ensemble-based system for face recognition in person re-identification. Mach. Vis. Appl. 26(6), 741–773 (2015)

    Article  Google Scholar 

  29. Ding, C., Chang, X., Tao, D.: Multi-task pose invariant face recognition. IEEE Trans. Image Process. 24(3), 980–993 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  30. Li, D., Zhou, H., Lam, K.-M.: High-resolution face verification using pore-scale facial features. IEEE Trans. Image Process. 24(8), 2317–2327 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  31. Ding, C., Choi, J., Tao, D., Davis, L.S.: Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 25(4), 518–531 (2016)

    Article  Google Scholar 

  32. Dasan, M., Alqudah, A., Debeir, O.: Face detection using Viola Jones method And Neural Networks. In: International Conference On Information And Communication Technology Research (ICTRC) (2015)

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jayam College of Engineering and Technology, Dharmapuri, India

    A. Vivek Yoganand

  2. Department of Physics, VeltechMultitech College of Engineering, Chennai, India

    A. Celine Kavida

  3. Department of Computer Science and Engineering, R.M.D. Engineering College, Tiruvallur, India

    D. Rukmanidevi

Authors
  1. A. Vivek Yoganand
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  2. A. Celine Kavida
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  3. D. Rukmanidevi
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Correspondence to A. Vivek Yoganand.

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Yoganand, A.V., Kavida, A.C. & Rukmanidevi, D. An efficient PCA based pose and occlusion invariant face recognition system for video surveillance. Cluster Comput 22 (Suppl 5), 11443–11456 (2019). https://doi.org/10.1007/s10586-017-1404-4

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  • DOI: https://doi.org/10.1007/s10586-017-1404-4

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