Face Spoofing Detection Using Faster R-CNN with Resnet50 Architecture on Video
Abstract
Face detection is a main and important process in the field of face recognition that has been widely studied. The purpose of face detection is to determine the presence and mark the position of faces, in both images and videos, called bounding boxes. One important problem in face detection is to differentiate between face spoof and non-spoof which is referred to as face spoofing detection. Face spoofing detection is an important task used to ensure the security of face-based authentication and facial analysis systems. Therefore, we need a model that can detect face spoofing. In this paper, the process to build a model that can be used to detect face spoofing on video is carried out using Faster R-CNN with Resnet50 architecture. Faster R-CNN is one of the superior algorithms in solving various object detection problems. The dataset used in this paper is a Replay-Attack Database, provided by Idiap Dataset Distribution Portal.The training phase used 360 videos, consisting of 300 spoof videos and 60 non-spoof videos. The average accuracy of the training stage is 97,07% with a total of 21 epochs. The test results show that the resulting model successfully determined bounding boxes and detected face spoof and non-spoof on the video effectively.
References
E. Owusu, J.D. Abdulai, dan Y. Zhan, “Face Detection Based on Multilayer Feed-forward Neural Network and Haar Features,” Softw. Pract. Exper, Vol. 49, No. 1, hal. 120-129, 2019.
Y. Zhou, D. Liu, dan T. Huang, “Survey of Face Detection on Low-quality Images,” 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2018, hal. 769-773.
G. Guo, H Wang, Y. Yan, J Zheng, dan B. Li, “A Fast Face Detection Method via Convolutional Neural Network,” Neurocomputing, Vol. 395, hal, 128-137, 2020.
X. Huang, “A Hybrid Framework for Human Face Detection and Recognition in Videos,” IOP Conf. Series: Earth and Environmental Science, Vol. 252, No. 5, hal. 1-5, 2018.
N.A. Othman dan I. Aydin, “A Face Recognition Method in the Internet of Things for Security Applications in Smart Homes and Cities,” 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), 2018, hal 20-24.
S.Y. Jin, A. RoyChowdhury, H. Jiang, A. Singh, A. Prasad, D. Chakraborty, dan E.L.Miller, “Unsupervised Hard Example Mining from Videos for Improved Object Detection,” European Conference on Computer Vision, 2018, hal. 1-18.
S. Ren, K. He, R.B. Girshick, dan J. Sun, “Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, hal. 1137-1149, 2017.
B. Benjdira, T. Khursheed, A. Koubaa, A. Ammar, dan K. Ouni, “Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3,” 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), 2019, hal. 1-6.
M.D. Putro, T.B. Adji, dan B. Winduratna, “Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones,” Sem. Nas. Sci. Eng. Technol., 2012, hal. 1–5.
R. Girshick, J. Donahue, T. Darrell, dan J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” IEEE Conf. on Computer Vision and Pattern Recognition, 2014, hal. 580-587.
Y. Liu, Z. Ma, X. Liu, S. Ma, dan K. Ren, “Privacy-Preserving Object Detection for Medical Images with Faster R-CNN,” IEEE Transactions on Information Forensics and Security, hal. 1-16, 2019.
S.M. Abbas dan S.N. Singh, “Region-based Object Detection and Classification Using Faster R-CNN,” Int. Conf. on Computational Intelligence and Communication Technology, 2018, hal. 1-6.
I. Chingovska, A. Anjos, dan S., Marcel, “On the Effectiveness of Local Binary Patterns in Face Non-spoofing,” Proc. of the Int. Conf. of Biometrics Special Interest Group (BIOSIG), 2012, hal. 1–7.
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