Classification Approach for Face Spoof Detection in Artificial Neural Network Based on IoT Concepts
Keywords:
Face Spoofing Techniques, Feature Classification, IOT, Artificial Neural Network (ANN), VGG-16Abstract
The topic of discussion in this piece is the convolutional neural network, often known as ANN. ANNs are a kind of artificial intelligence that can acquire new knowledge and recognise even the most subtle of differences in the data they are given to analyse. Deep convolutional neural networks are responsible for a significant number of the recent breakthroughs that have been achieved in the field of image classification. This work presents a number of different ANN designs, most of which make extensive use of convolutional layers, in order to identify face spoofing. We "teach" it for an application-specific domain for which there are few training examples by first "training" a deep neural network with a vast quantity of labelled data, and then "teaching" it for the deep neural network itself. This is done in the order of "training" and "teaching." This will allow us to achieve our goal. After that, we create training sample pairs for the network distillation using samples from both domains. We "train" a deep neural network by "training" it with a tone of labelled data at first, and then we "teach" it for a particular application area for which there aren't enough training in IoT. By doing this, we "train" it. The more technical term "teaching" a deep neural network is the one that is used most often to describe this process. The two domains may be compared with one another. First things first: if we want to be able to train a discriminative deep neural network on an application-specific domain, we need to gather data that is unique to spoofing. The proposed technique has been tested in a number of different ways and has shown to be effective when combined with anti-spoofing settings.
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J. C. Neves, R. Tolosana, R. Vera-Rodriguez, V. Lopes, H. Proen¸ca, and J. Fierrez, “Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detection,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 1038–1048, 2020.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “The journal of machine learning research,” vol. 15, pp. 1943–1955, 2014.
X. Zhang, J. Zou, K. He, and J. Sun, “Accelerating very deep convolutional networks for classification and detection.” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 10, pp. 1943–1955, 2016. [Online]. Available: http://dblp. uni-trier.de/db/journals/pami/pami38.html#ZhangZHS16.
L. Feng, L.-M. Po, Y. Li, X. Xu, F. Yuan, T. C.-H. Cheung, and K.-W. Cheung, “Integration of image quality and motion cues for face antispoofing: A neural network approach,” Journal of Visual Communication and Image Representation, vol. 38, pp. 451–460, 2016.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Scholkopf, and A. Smola, ¨ “A kernel two-sample test,” The Journal of Machine Learning Research, vol. 13, no. 1, pp. 723–773, 2012.
I. Chingovska, A. Anjos, S. Marcel, On the effectiveness of local binary patterns in face anti-spoofing, in: Proc. International Conference of the Biometrics Special Interest Group, Darmstadt, Germany, 2012, pp. 1–7.
Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022
Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219
Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729
Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321
Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774
Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721
A. Benlamoudi, D. Samai, A. Ouafi, S. E. Bekhouche, A. Taleb-Ahmed, and A. Hadid, “Face spoofing detection using local binary patterns and fisher score,” in 2015 3rd International Conference on Control, Engineering Information Technology (CEIT), 2015, pp. 1–5. doi: 10.1109/CEIT.2015.7233145.
M. Asim, Z. Ming, and M. Y. Javed, “ANN based spatio-temporal feature extraction for face anti-spoofing,” 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 234–238, 2017.
M. Asim, Z. Ming, and M. Y. Javed, “ANN based spatio-temporal feature extraction for face anti-spoofing,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), IEEE, 2017, pp. 234–238.
A. Benlamoudi, D. Samai, A. Ouafi, S. E. Bekhouche, A. Taleb-Ahmed, and A. Hadid, “Face spoofing detection using local binary patterns and fisher score,” in 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), IEEE, 2015, pp. 1–5.
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