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Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement

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Proceedings of International Conference on Trends in Computational and Cognitive Engineering


Face detection and recognition is a prevalent concept in security and access control area which is commonly used in surveillance cameras at public places, attendance etc. But often this type of system can be circumvented by holding a photo or running a video of authorized person to the camera. Therefore, liveliness concept comes up with a solution to detect the person is real or spoofed. In this paper, We proposed a cascade classifier based model for detecting liveliness using deep-learning and Heart-beat measurement. Moreover, we have evaluated our model accuracy with our own dataset of real and fake videos and photos. By using our proposed model of face liveliness detection model, FPR and FNR have declined 16% and 5.22% respectively. In addition, we have also compared proposed system with other state-of-art methods. And here proposed study has achieved an accuracy of 99.46%.

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  1. Zulfiqar, M., Syed, F., Khan, M.J., Khurshid, K.: Deep face recognition for biometric authentication. In: 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. 1–6, July 2019

    Google Scholar 

  2. Mamun, S.A., Shamim Kaiser, M., Ahmed, M.R., Islam, Md. S., Islam, Md. I.: Performance analysis of optical wireless communication system employing neuro-fuzzy based spot-diffusing techniques. Commun. Netw, 5(3), 260–265, 2013-09-27 (Number: 3 Publisher: Scientific Research Publishing)

    Google Scholar 

  3. Biswas, S., Kaiser, M.S., Mamun, S.A.: Applying ant colony optimization in software testing to generate prioritized optimal path and test data. In: 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–6 (2015)

    Google Scholar 

  4. Shamim Kaiser, M., Chowdhury, Z.I., Al Mamun, S., Hussain, A., Mahmud, M: A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Cogn. Comput. 8(5), 946–954 (2016)

    Google Scholar 

  5. Fatemifar, S., Arashloo, S.R., Awais, M., Kittler, J.: Spoofing attack detection by anomaly detection. In: ICASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8464–8468, May 2019. ISSN 2379-190X

    Google Scholar 

  6. Al Mamun, S., Suzuki, R., Lam, A., Kobayashi, Y., Kuno. Y.: Terrain recognition for smart wheelchair. In: Huang, D., Han, K., Hussain, A. (eds.) Intelligent Computing Methodologies. Lecture Notes in Computer Science, pp. 461–470. Springer (2016)

    Google Scholar 

  7. Al Mamun, S., Fukuda, H., Lam, A., Kobayashi, Y., Kuno, Y.: Autonomous bus boarding robotic wheelchair using bidirectional sensing systems. In: Advances in Visual Computing. Lecture Notes in Computer Science, pp. 737–747. Springer (2018)

    Google Scholar 

  8. Kaiser, M.S., et al.: Advances in crowd analysis for urban applications through urban event detection. IEEE Trans. Intell. Transport. Syst. 19(10), 3092–3112 (2017)

    Google Scholar 

  9. Kim, G., Eum, S., Suhr, J.K., Kim, D.I., Park, K.R., Kim, J.: Face liveness detection based on texture and frequency analyses. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 67–72, March 2012. ISSN 2376-4201

    Google Scholar 

  10. Komulainen, J., Hadid, A., Pietikäinen, M.: Context based face anti-spoofing. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8, Sept 2013

    Google Scholar 

  11. Kim, S., Ban, Y., Lee, S.: Face liveness detection using defocus. Sensors 15(1), 1537–1563, Jan 2015 (Number: 1 Publisher: Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  12. Chingovska, I., dos Anjos, A.R.: On the use of client identity information for face antispoofing. IEEE Trans. Inf. Forensics Secur. 10(4), 787–796, April 2015 (Conference Name: IEEE Transactions on Information Forensics and Security)

    Google Scholar 

  13. Li, X., Komulainen, J., Zhao, G., Yuen, P.-C., Pietikäinen, M.: Generalized face anti-spoofing by detecting pulse from face videos. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 4244–4249, Dec 2016

    Google Scholar 

  14. Ostankovich, V., Prathap, G., Afanasyev, I.: Towards human pulse rate estimation from face video: automatic component selection and comparison of blind source separation methods. In 2018 International Conference on Intelligent Systems (IS), pp. 183–189 (2018)

    Google Scholar 

  15. Kollreider, K., Fronthaler, H., Faraj, M.I., Bigun, J.: Real-time face detection and motion analysis with application in “Liveness” assessment. IEEE Trans. Inf. Forensics Secur. 2(3), 548–558, Sept 2007. (Conference Name: IEEE Transactions on Information Forensics and Security)

    Google Scholar 

  16. Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. arXiv:1605.07678 [cs], Apr 2017.

  17. Zulfiker, Md. S., Kabir, N., Biswas, A.A., Chakraborty, P., Rahman, Md. M.: Predicting students’ performance of the private universities of Bangladesh using machine learning approaches. Int. J. Adv. Comput. Sci. Appl. 11(3) (2020)

    Google Scholar 

  18. Akbulut, Y., Sengur, A., Budak, Ü., Ekici, S.: Deep learning based face liveness detection in videos. In: IDAP, pp. 1–4, Sept 2017

    Google Scholar 

  19. Paul, M.C., Sarkar, S., Rahman, M.M., Reza, S.M., Kaiser, M.S.: Low cost and portable patient monitoring system for e-health services in Bangladesh. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–4 (2016)

    Google Scholar 

  20. Rehman, Y.A.U., Po, L.-M., Liu, M., Zou, Z., Weifeng, O., Zhao, Y.: Face liveness detection using convolutional-features fusion of real and deep network generated face images. J. Vis. Commun. Image Represent. 59, 574–582 (2019). February

    Article  Google Scholar 

  21. Koshy, R., Mahmood, A.: Optimizing deep CNN architectures for face liveness detection. Entropy 21(4), 423, Apr 2019 (Number: 4 Publisher: Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  22. de Souza, G.B., Papa, J.P., Marana, A.N.: On the learning of deep local features for robust face spoofing detection. arXiv:1806.07492 [cs, stat], Oct 2018.

  23. Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Mamun, S.A., Mahmud, M.: Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Inf. 7(1), 1–21 (2020)

    Google Scholar 

  24. van Gent, P., Farah, H., Nes, N., Arem, B.: Heart rate analysis for human factors: development and validation of an open source toolkit for noisy naturalistic heart rate data. In: 6th Humanist Conference, 06 2018

    Google Scholar 

  25. Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. In: Lee, S.-W., Li, S.Z. (eds.) Advances in Biometrics, Lecture Notes in Computer Science, pp. 252–260. Springer, , Berlin, Heidelberg (2007)

    Google Scholar 

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Correspondence to Md. Mahfujur Rahman .

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Rahman, M.M., Mamun, S.A., Kaiser, M.S., Islam, M.S., Rahman, M.A. (2021). Cascade Classification of Face Liveliness Detection Using Heart Beat Measurement. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore.

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