These are the sources and citations used to research Artificial intelligent. This bibliography was generated on Cite This For Me on
In-text: (, A and , I, 2012)
Your Bibliography: , A, K. and , I, S., 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems.
In-text: (Convolutional Neural Networks for Visual Recognition, 2018)
Your Bibliography: 2018. Convolutional Neural Networks for Visual Recognition. Stanford CS231n.
In-text: (Ahonen, Hadid and Pietikainen, 2006)
Your Bibliography: Ahonen, T., Hadid, A. and Pietikainen, M., 2006. Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), pp.2037-2041.
In-text: (Ansari and Abdel-Mottaleb, 2005)
Your Bibliography: Ansari, A. and Abdel-Mottaleb, M., 2005. Automatic facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition. Pattern Recognition, 38(12), pp.2549-2563.
In-text: (Face Recognition: Issues, Methods and Alternative Applications | IntechOpen, 2018)
Your Bibliography: Bing.com. 2018. Face Recognition: Issues, Methods and Alternative Applications | IntechOpen. [online] Available at: <https://www.bing.com/cr?IG=B875F82267864494892A125611615D02&CID=3A5523E8075466BC114B280C0604671D&rd=1&h=z22uwBC1RtJeiKaZiEnqrjDTgeaRlaFIIN6dKN2eaoQ&v=1&r=https%3a%2f%2fwww.intechopen.com%2fbooks%2fface-recognition-semisupervised-classification-subspace-projection-and-evaluation-methods%2fface-recognition-issues-methods-and-alternative-applications&p=DevEx.LB.1,5516.1> [Accessed 3 May 2018].
In-text: (Drira et al., 2013)
Your Bibliography: Drira, H., Ben Amor, B., Srivastava, A., Daoudi, M. and Slama, R., 2013. 3D Face Recognition under Expressions, Occlusions, and Pose Variations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9), pp.2270-2283.
In-text: (Face Recognition: Issues, Methods and Alternative Applications | IntechOpen, 2016)
Your Bibliography: Intechopen.com. 2016. Face Recognition: Issues, Methods and Alternative Applications | IntechOpen. [online] Available at: <https://www.intechopen.com/books/face-recognition-semisupervised-classification-subspace-projection-and-evaluation-methods/face-recognition-issues-methods-and-alternative-applications> [Accessed 4 May 2018].
In-text: (Li, Correia and Hadid, 2018)
Your Bibliography: Li, L., Correia, P. and Hadid, A., 2018. Face recognition under spoofing attacks: countermeasures and research directions. IET Biometrics, 7(1), pp.3-14.
In-text: (Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning, 2018)
Your Bibliography: Medium. 2018. Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning. [online] Available at: <https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78> [Accessed 4 May 2018].
In-text: (Molyneux, Kamuya and Marsh, 2010)
Your Bibliography: Molyneux, S., Kamuya, D. and Marsh, V., 2010. Community Members Employed on Research Projects Face Crucial, Often Under-Recognized, Ethical Dilemmas. The American Journal of Bioethics, 10(3), pp.24-26.
In-text: (R Nayak, Surve2, Prabhu and Agwanka, 2014)
Your Bibliography: R Nayak, R., Surve2, D., Prabhu, P. and Agwanka, N., 2014. Reduction in Computational Complexity-A Face Recognition Case Study. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3,(Issue 2,).
In-text: (Ravibabu and .N.Krishnan, 2018)
Your Bibliography: Ravibabu, V. and .N.Krishnan, D., 2018. A vary approach to face recognition veritable mechanisms for Android mobile against spoofing - IEEE Conference Publication. Bing.com.
In-text: (Shen et al., 2017)
Your Bibliography: Shen, Y., Yang, M., Wei, B., Chou, C. and Hu, W., 2017. Learn to Recognise: Exploring Priors of Sparse Face Recognition on Smartphones. IEEE Transactions on Mobile Computing, 16(6), pp.1705-1717.
In-text: (Toygar and Acan, 2004)
Your Bibliography: Toygar, Ö. and Acan, A., 2004. Multiple classifier implementation of a divide-and-conquer approach using appearance-based statistical methods for face recognition. Pattern Recognition Letters, 25(12), pp.1421-1430.
In-text: (Wu, Liu, Li and Fu, 2018)
Your Bibliography: Wu, Y., Liu, H., Li, J. and Fu, Y., 2018. Deep Face Recognition with Center Invariant Loss.
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