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A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling

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Proceedings of International Joint Conference on Computational Intelligence

Abstract

In this paper several face detection algorithms are compared on the basis of mathematical analysis to find out the most efficient algorithm. At first the mathematical model of different face detection algorithms (Camshift, AdaBoost, LBP and Viola Jones algorithms) are analyzed and compared to find out the most efficient one. Mathematical results show that Viola Jones performs best result to detect the face. But in case of Viola Jones, integral image integrates the non-face region pixels with face region pixels as a result, the pixel value redundancy is occurred which degrades its efficiency. To overcome this problem, a new face detection algorithm is proposed in this paper which is named as Break Point Value (BPV) algorithm. The mathematical model of our proposed method is derived where integral images are compared with Local Binary Pattern (LBP) and the compared value is suggested as test value. If the test value is less than or equal to the BPV then the region is a face region and if it is not, the region is a non-face region. Since there is a comparison between integral image value and LBP value of the same pixel region the redundant values are reduced. Furthermore, the use of BPV helps to find out more relevant frames. Thus the proposed method is more efficient face detection process as compared to the previous processes in the field of face detection system.

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References

  1. Képešiová Z, Kozák S (2018) An effective face detection algorithm. In: Proceedings of the 29th international conference 2018 cybernetics & informatics (K&I) Jan 31–Feb 3, 2018, Lazy pod Makytou, Slovakia

    Google Scholar 

  2. Siddiquee KNEA, Sarma D, Nandi A, Akhter S, Hossain S, Andersson K, Hossain MS (2017) Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method. In: 12th IEEE conference on industrial electronics and applications (ICIEA), pp 258–263

    Google Scholar 

  3. Fradi H, Dugelay JL (2013) A new multiclass SVM algorithm and its application to crowd density analysis using LBP features. In: IEEE international conference on image processing (ICIP), pp 4556–4557

    Google Scholar 

  4. Ranganatha S, Gowramma YP (2017) an integrated robust approach for fast face tracking in noisy real-world videos with visual constraints. In: International conference on intelligent computing and control (I2C2), pp 772–776

    Google Scholar 

  5. Bradski GR (1998) Computer vision face tracking for use in perceptual user interface. Intell Technol J 2(2):13–27

    Google Scholar 

  6. Fukunaga K, Hostetler LD (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Informat Theor 21:32–40

    Article  MathSciNet  Google Scholar 

  7. Zhang C, Qiao Y, Fallon E, Xu C (2009) An improved CamShift algorithm for target tracking in video surveillance. In; 9th IT&T conference, Dublin Institute of Technology, Dublin, Ireland

    Google Scholar 

  8. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  9. Turk M, Pentland A (1991) Eigen faces for recognition. J Cogn Neurosci 3:71–86

    Article  Google Scholar 

  10. Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. J Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  11. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  12. Ranganatha S, Gowramma YP (2017) An integrated robust approach for fast face tracking in noisy real-world videos with visual constraints. In: International conference on advances in computing, communications and informatics (ICACCI), pp 772–776

    Google Scholar 

  13. Fradi H, Dugelay JL (2013) A new multiclass SVM algorithm and its application to crowd density analysis using LBP features. In: IEEE international conference on image processing, pp 4556–4557

    Google Scholar 

  14. Savaş BK, İlkin S, Becerikli Y (2016) The realization of face detection and fullness detection in medium by using haar cascade classifiers. In: IEEE signal processing and communication application conference (SIU), pp 2217–2220

    Google Scholar 

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Correspondence to Tangina Sultana .

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Sultana, T., Hossain, M.D., Zead, N.H., Sarker, N.A., Fardoush, J. (2020). A New Approach for Efficient Face Detection Using BPV Algorithm Based on Mathematical Modeling. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_30

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