Abstract
Face detection comes under the domain of object detection and tracking. Face detection is an integral part of the motion based object detection which combines digital image processing and computer vision for the detection of instances and faces as well. This paper provides a brief overview of the recent trends; current open challenging issues and their solutions available for efficient detection of faces form video stream or still images. This paper also discusses various approaches which are widely used to detect the faces in the dynamic background, illumination and other current challenges. In the last section, a framework for face detection is also proposed using SVM classifier.
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References
Wang, M., Wang, Z., Li, J.: Deep convolutional neural network applies to face recognition in small and medium databases. In: 2017 4th International Conference on Systems and Informatics (ICSAI), pp. 1368–1372 (2018)
Triantafyllidou, D., Tefas, A.: Face detection based on deep convolutional neural networks exploiting incremental facial part learning. In: 23rd International Conference on Pattern Recognition (ICPR), pp. 3560–3565 (2017)
Er, M.J., Wu, S., Lu, J., Toh, H.L.: Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Netw. 13, 697–710 (2002)
Aziz, K.A.A., Ramlee, R.A., Abdullah, S.S., Jahari, A.N.: Face detection using radial basis function neural networks with variance spread value. In: 2009 International Conference of Soft Computing and Pattern Recognition, pp. 399–403 (2009)
Yoo, S.H., Oh, S.K., Pedrycz, W.: Optimized face recognition algorithm using radial basis function neural networks and its practical applications. Neural Netw. 69, 111–125 (2015)
Kim, K., et al.: Face recognition using support vector machines with local correlation kernels. Int. J. Pattern Recogn. Artif. Intell. 16, 97–111 (2002)
Zhou, S.K., Chellappa, R.: Multiple-exemplar discriminant analysis for face recognition. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 4, pp. 191–194 (2004)
Ohlyam, S., Sangwan, S., Ahuja, T.: A survey on various problem & challenges in face recognition. Int. J. Eng. Res. Technol. 2(6), 2533–2538 (2013)
Tsai, C.C., et al.: Face detection using eigenface and neural network. In: 2006 IEEE International Conference on Systems, Man and Cybernetics, pp. 4343–4347 (2007)
Sharifara, A., et al.: A general review of human face detection including a study of neural networks and haar feature–based cascade classifier in face detection. In: 2014 International Symposium on Biometric and Security Technologies (ISBAST 2015) (2015)
Tayyab, M., Zafar, M.F.: Face detection using 2D-discrete cosine transform and back propagation neural network. In: 2009 International Conference of Emerging Technologies (2009)
El-Bakry, H.M.: Face detection using neural networks and image decomposition. In: Proceedings of International Joint Conference on Neural Networks, IJCNN 2002 (2002)
Jamil, N., Iqbal, S., Iqbal, N.: Face recognition using Neural Networks. In: Proceedings of IEEE International Multi Topic Conference, Technology for the 21st Century, pp. 416–419 (2001)
Huang, D.Y., Chen, C.H., Chen, T.Y.: Real-time face detection using a moving camera. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (2018)
Dang, K., Sharma, S.: Review and comparison on face detection algorithms. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering – Conference (2017)
Fernandez, M.C.D., et al.: Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification. In: 2014 IEEE Region 10 Symposium (2014)
Hilado, S.D.F., Dadios, E.P.: Face detection using neural networks with skin segmentation. In: 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), pp. 261–265 (2011)
Lang, L., Gu, W.: Study on face detection algorithm for real-time face detection system. In: 2009 Second International Symposium on Electronic Commerce and Security (2009)
Face Detection Technologies. https://disruptionhub.com/5-applications-facial-recognition-technology/. Accessed 15 Oct 2018
Sharma, L., Lohan, N.: Performance analysis of moving object detection using BGS techniques in visual surveillance. Int. J. Spatio-Temporal Data Sci. Indersci. 1(1), 22–53 (2019)
Sharma, L., Yadav, D.K.: Histogram based adaptive learning rate for background modelling and moving object detection in video surveillance. Int. J. Telemed. Clin. Pract. Indersci. 2(1), 74–92 (2017)
Sharma, L., Lohan, N., Yadav, D.K.: A study of challenging issues on video surveillance system for object detection. J. Basic Appl. Eng. Res. 4(4), 313–318 (2017)
Sharma, L., Singh, S., Yadav, D.K.: Fisher’s linear discriminant ratio based threshold for moving human detection in thermal video. Infrared Phys. Technol. 78, 118–128 (2016)
Sharma, L., Yadav, D.K., Bharti, S.: An improved method for visual surveillance using background subtraction technique. In: IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN 2015), pp. 421–426. Amity University Noida, India (2015)
Yadav, D.K., Sharma, L., Bharti, S.: Fuzzy-rule based threshold for moving human detection in video. In: International Conference on Advanced and Agile Manufacturing (ICAM 2015) (2015)
Yadav, D.K., Sharma, L., Bharti, S.: Moving object detection in real-time visual surveillance using background subtraction technique. In: IEEE 14th International Conference in Hybrid Intelligent Computing (HIS 2014), pp. 79–84. Gulf University for Science and Technology, Kuwait (2014)
Min, R., Kose, N., Dugelay, J.L.: KinectFaceDB: a kinectdatabase for face recognition. IEEE Trans. Syst. Man Cybern.: Syst. 44(11), 1534–1548 (2014)
Zohra, F.T., et al.: Occlusion detection and localization from kinect depth images. In: 2016 International Conference on Cyberworlds, pp. 189–196 (2016)
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Makkar, S., Sharma, L. (2019). A Face Detection Using Support Vector Machine: Challenging Issues, Recent Trend, Solutions and Proposed Framework. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_1
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