Abstract
In this paper, we proposed a novel, cost-effective and energy-efficient framework by introducing a Raspberry Pi-based identity verification through face recognition in an offline mode. A Raspberry Pi device and mobile phone is wirelessly connected to standard pocket WIFI to process the face detection and face verification. The experimental tests were done using 3000 public constrained images. The proposed method is implemented on Raspberry Pi 3 run in Python 3.7 where the datasets and trained datasets were experimented using LBP algorithm for face detection and face verification in five split testing. The result was interpreted using the confusion matrix and Area Under the Curve (AUC) and Receiver Operating Characteristics (ROC). To sum up, the proposed method showed an average result of 0.98135% accuracy, with 98% recall score and an F1-score of 0.9881. During the offline mode testing, the face detection and verification average timing is 1.4 s.
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References
Ohlyan, S., Sangwan, S., Ahuja, T.: A survey on various problems & challenges in face recognition. Int. J. Eng. Res. Technol. (IJERT) 2(6), 2533–2538 (2013)
Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wireless Commun. Mobile comput. 13(18), 1587–1611 (2013)
Virata, A.J.A., Festijo, E.D.: Understanding the acceptance of a face recognition-based identity verification system: an input to its design and development in the Philippines. In: 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), pp. 218–222. IEEE, February 2019
Khan, A.N., Kiah, M.M., Khan, S.U., Madani, S.A.: Towards secure mobile cloud computing: a survey. Future Generation Comput. Syst. 29(5), 1278–1299 (2013)
Huerta-Canepa, G., Lee, D.: A virtual cloud computing provider for mobile devices. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond, p. 6. ACM, June 2010
Rimal, B.P., Choi, E., Lumb, I.: A taxonomy and survey of cloud computing systems. In: 2009 Fifth International Joint Conference on INC, IMS and IDC, pp. 44–51. IEEE, August 2009
Yang, G., Huang, T.S.: Human face detection in a complex background. Pattern Recogn. 27(1), 53–63 (1994)
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427–436 (2015)
Hjelmås, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001)
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: Openface: a general-purpose face recognition library with mobile applications. CMU School Comput. Sci., 6 (2016)
Lei, Z., Li, S.Z.: Face recognition models: computational approaches (2015)
Chellappa, R., Du, M., Turaga, P., Zhou, S.K.: Ace tracking and recognition in video. In: Handbook of Face Recognition, pp. 323–351. Springer, London (2011)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and pattern recognition, pp. 3476–3483 (2013)
Zhang, C., Zhang, Z.: A survey of recent advances in face detection (2010)
Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. Jips 5(2), 41–68 (2009)
Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. J. Inf. Process. Syst. 5(2), 41–68 (2009)
Ahonen, T., Pietikainen, M., Hadid, A.: Face recognition with local binary pattern, ECCV (2004)
Bereta, M., Pedrycz, W., Reformat, M.: Local descriptors and similarity measures for frontal face recognition: a comparative analysis. J. Vis. Commun. Image Represent. 24(8), 1213–1231 (2013)
Ting, K.M.: Confusion matrix. In: Encyclopedia of Machine Learning and Data Mining, p. 260 (2017)
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Virata, A.J.A., Festijo, E.D. (2020). A Raspberry Pi-Based Identity Verification Through Face Recognition Using Constrained Images. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_24
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DOI: https://doi.org/10.1007/978-3-030-52246-9_24
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