Abstract
This paper investigates the performance of the real-time face recognition system with machine learning, as well as the performance of each haarcascade classifiers that based on accuracy and speed. We employ the subset of machine learning called deep learning to the real-time face recognition system as the deep face recognition technique has improved the state-of-the-art performance. This system uses a pre-trained model named FaceNet and employs triplet loss technique to impose a margin between every pair of faces from the same person to other faces. In other words, it minimizes the distance between the anchor and the positive from the same identity and maximizes the distance between the anchor and the negative from different identity. Furthermore, to further investigate the performance of the system, we implement the Tensorflow framework to improve the system performance by the usage of Graphics Processing Unit (GPU). This system uses Labeled Faces in Wild (LFW) dataset as the benchmark to test the performance of the face recognition system. Apart from that, we conduct a preliminary experiment to evaluate the performance of haarcascade classifiers so that we can choose the best classifier in term of accuracy and speed. Haarcascade_frontalface_default.xml (FD) exhibits best performance compared to haarcascade_frontalface_alt.xml (FA) and haarcascade_frontalface_alt2.xml (FA2) with accurate number of faces detected and shortest average time taken to detect faces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Trigueros, D.S., Meng, L., Hartnett, M.: Face recognition: from traditional to deep learning methods (2018). arXiv:1811.00116v1
Sukri, S.S., Ruhaiyem, N.I.R., Mohamed, A.S.A.: Face recognition with real time eye lid movement detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2017). https://doi.org/10.1007/978-3-319-70010-6_33
Wagh, P., et al.: Attendance system based on face recognition using eigen face and PCA algorithms. In: Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015, pp. 303–308 (2016). https://doi.org/10.1109/icgciot.2015.7380478
Ara, N.M., Simul, N.S., Islam, M.S.: Convolutional neural network approach for vision based student recognition system. In: 20th International Conference of Computer and Information Technology, ICCIT 2017, 2018–January, pp. 1–6 (2018). https://doi.org/10.1109/iccitechn.2017.8281789
Arsenovic, M., et al.: FaceTime - deep learning based face recognition attendance system. In: Proceedings of SISY 2017 - IEEE 15th International Symposium on Intelligent Systems and Informatics, pp. 53–57 (2017). https://doi.org/10.1109/sisy.2017.8080587
Neeraj, S., Sugandha, S.: Survey of advanced face detection techniques in image processing. Int. J. Adv. Res. Comput. Commun. Eng. 2(1), 22–24 (2014). http://www.ijcsmr.org/vol1issue2/paper26.pdf
Bagga, M., Singh, B.: Spoofing detection in face recognition: a review. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 2037–2042 (2016)
Kaur, R.: Techniques of face spoof detection: a review. Int. J. Comput. Appl. 164(1), 29–33 (2017)
Luo, X., et al.: A deep convolutional neural network model for vehicle recognition and face recognition. In: International Congress of Information and Communication Technology (ICICT 2017) (2017)
Santoso, K., Kusuma, G.P.: Face recognition using modified OpenFace. Procedia Computer Science 135, 510–517 (2018). https://doi.org/10.1016/j.procs.2018.08.203
Erdogmus, N., Marcel, S.: Spoofing face recognition with 3D masks. IEEE Trans. Inf. Forensics Secur. 9(7), 1084–1097 (2014). https://doi.org/10.1109/TIFS.2014.2322255
Soukupová, T., Cech, J.: Real-time eye blink detection using facial landmarks (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet : a unified embedding for face recognition and clustering. 1, 2014 (2014)
Xia, X., Xu, C., Nan, B.: Facial expression recognition based on Tensorflow platform, vol. 01005, pp. 8–11 (2017)
Cuimei, L., Zhiliang, Q., Nan, J., Jianhua, W.: Human face detection algorithm via Haar cascade classifier combined with three additional classifiers. In: 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) (2017). https://doi.org/10.1109/icemi.2017.8265863
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sukri, S.S., Ruhaiyem, N.I.R. (2019). iFR: A New Framework for Real-Time Face Recognition with Deep Learning. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2019. Lecture Notes in Computer Science(), vol 11870. Springer, Cham. https://doi.org/10.1007/978-3-030-34032-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-34032-2_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34031-5
Online ISBN: 978-3-030-34032-2
eBook Packages: Computer ScienceComputer Science (R0)