Abstract
Face recognition is an important concept, which has generally considered in the course of recent decades. Generally image location can be considered as an extraordinary sort of item recognition in PC vision. In this paper, we explore one of the vital and very effective systems for conventional item discovery using convolutional neural network (CNN) method, that is, a gentle classifier development for resolving the substance identification problem. That in recognizing the face of images as the problem is very difficult one, and so far no quality results are been obtained. Usually, this problem splits into distinctive sub-issues, to make it simpler to work predominantly identification of face of a picture pursued by the face acknowledgment itself. There are several tasks to perform in between such as partial image face detection or extracting more features from them. Many years there are numerous calculations and systems have been utilized such as eigenfaces or active shape model, principal component analysis (PCA), K-nearest neighbour (KNN), and local binary pattern histograms (LBPH), but accurate results have not been identified. However because of the drawbacks of previously mentioned techniques in my study, I want to use CNN in deep learning to obtain best results.
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
Change history
11 March 2024
A correction has been published.
References
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)
Taigman, Y, Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Kumar, K.S., Semwal, V.B., Tripathi, R.: Real time face recognition using adaboost improved fast PCA algorithm, arXiv preprint arXiv:1108.1353
Parkhi,O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference, vol. 1, p. 6 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 511. IEEE (2001)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Kamencay, P., et al.: A new method for face recognition using convolutional neural network (2017)
Eleyan, A., Demirel, H.: PCA and LDA based face recognition using feedforward neural network classifier. In: MRCS 2006, LNCS 4105, pp. 199—206. © Springer-Verlag Berlin Heidelberg (2006)
Li, G., Zhang, J., Wang, Y., Freeman, W.J.: Face recognition using a neural network simulating olfactory systems. UC Berkely Previously Published Works( 2006)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12) (2006)
Gandhe, S.T., Talele, K.T., Keskar, A.G.: Intelligent face Recognition techniques: a comparative study. GVIP J. 7(2) (2007)
Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2D and 3D face recognition: a survey. Elsevier (2007)
Bhuiyan, A.-A., Liu, C.H.: On face recognition using gabor filters. World Academy of Science, Engineering and Technology (2007)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S.: Robust face recognition via sparse representation. IEEE Trans. (2008)
Bashyal, S., Venayagamoorthy, G.K.: Recognition of facial expressions using gabor wavelets and learning vector quantization. Elsevier (2008)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S, Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2) (2009)
Latha. P., Ganesan, L., Annadurai, S.: Face recognition using neural networks. Signal Process. Int. J. (SPIJ) 3(5), 153–160 (2009)
Anam, S., Islam, M.S., Kashem, M.A.,Islam, M.N., Islam, M.R, Islam, M.S.: Face recognition using genetic algorithm and back propagation neural network. In: Proceedings of the International MultiConference of Engineers and Computer Scientists (2009)
Agarwal, M., Jain, N., Kumar, M., Agrawal, H.: Face recognition using Eigen faces and artificial neural network. IJCTE 2(4) (2010)
Gumus, E., Kilic, N., Sertbas, A., Ucan, O.N.: Evaluation of face recognition techniques using PCA wavelets and SVM. Elsevier (2010)
Paisitkriangkrai, S., Shen, C., Zhang, J.: Incremental training of a detector using online sparse eigendecomposition. IEEE Trans. Image Process. 20(1) (2011)
Hajati, F., Raie, A.A., Gao, Y.: Pose-invariant multimodal (2D + 3D) face recognition using geodesic distance map. J. Am. Sci. 7(10) (2011)
Ahmad, F., Najam, A., Ahmed, Z.: Image-based face detection and recognition: “state of the art”. IJCSI 9(6, no. 1) (2012)
Kalaimagal, S., Singh, M.E.J.: Face recognition using Gabor volume based local binary pattern. IJARCSSE 2(6) (2012)
Kahou, S.E., et al.: Combining modality specific deep neural networks for emotion recognition in video. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction. ACM (2013)
Sharif Razavian, A., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014)
Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2016)
Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vision 126(2–4), 144–157 (2018)
Sharma, Y.K., Rathore, R.S.: Fabrication analysis for corner identification using algorithms increasing the production rate. Int. J. Comput. Sci. Eng. (IJCSE) 9(05), 248–256 (2017). ISSN: 0975-3397
Liu, W., et al.: Sphereface: deep hypersphere embedding for face recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1 (2017)
Sharma, Y.K., Rathore, R.S.: Advanced grading system for quality assurance of ceramic tiles based using digital image processing. Indian J. Sci. Technol. 10(14), 1–4 (2017). https://doi.org/10.17485/ijst/2017/v10i14/112248. ISSN (Online):0974-5645, (Print): 0974-6846
Sun, X., Wu, P., Hoi, S.C.H.: Face detection using deep learning: an improved faster RCNN approach. Neurocomputing 299, 42–50 (2018)
Krishnamurthy, G., et al.: A deep learning approach for multimodal deception detection. arXiv preprint arXiv:1803.00344 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Swapna, M., Sharma, Y.K., Prasad, B.M.G. (2020). RETRACTED CHAPTER: A Survey on Face Recognition Using Convolutional Neural Network. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_54
Download citation
DOI: https://doi.org/10.1007/978-981-15-1097-7_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1096-0
Online ISBN: 978-981-15-1097-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)