Proceedings of 2nd International Conference on Computer Vision & Image Processing pp 317-330 | Cite as
Analyzing ConvNets Depth for Deep Face Recognition
- 480 Downloads
Abstract
Deep convolutional neural networks are becoming increasingly popular in large-scale image recognition, classification, localization, and detection. In this paper, the performance of state-of-the-art convolution neural networks (ConvNets) models of the ImageNet challenge (ILSVRC), namely VGG16, VGG19, OverFeat, ResNet50, and Inception-v3 which achieved top-5 error rates up to 4.2% are analyzed in the context of face recognition. Instead of using handcrafted feature extraction techniques which requires a domain-level understanding, ConvNets have the advantages of automatically learning complex features, more training time, and less evaluation time. These models are benchmarked on AR and Extended Yale B face dataset with five performance metrics, namely Precision, Recall, F1-score, Rank-1 accuracy, and Rank-5 accuracy. It is found that GoogleNet ConvNets model with Inception-v3 architecture outperforms than other four architectures with a Rank-1 accuracy of 98.46% on AR face dataset and 97.94% accuracy on Extended Yale B face dataset. It confirms that deep CNN architectures are suitable for real-time face recognition in the future.
Keywords
Deep learning Face recognition Convolutional neural networks Computer visionNotes
Acknowledgements
This research project is supported by DAE-BRNS, Department of Atomic Energy, Government of India. The authors would like to extend their sincere thanks to DAE-BRNS for their support.
References
- 1.T. Ojala, M. Pietikäinen and T. Mäenpää, “Multiresolution Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.Google Scholar
- 2.D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 91–110. https://doi.org/10.1023/b:visi.0000029664.99615.94, 2004.
- 3.N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, 886–893, 2005.Google Scholar
- 4.J. Yang, Y.G. Jiang, A.G. Hauptmann, and C.W. Ngo, “Evaluating Bag-of-Visual Words Representations in Scene Classification”, Proceedings of the International Workshop on Multimedia Information Retrieval (ACM), 197–206, 2007.Google Scholar
- 5.A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Conference on Neural Information Processing Systems (NIPS), pp. 1106–1114, 2012.Google Scholar
- 6.K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, International Conference on Learning Representations (ICLR), 2015.Google Scholar
- 7.C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper with Convolutions”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.Google Scholar
- 8.P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated Recognition, Localization and Detection using Convolutional Networks”, International Conference on Learning Representations (ICLR), 2014.Google Scholar
- 9.K. He, X. Zhang, S. Ren, J. Sun, “Deep Residual Learning for Image Recognition”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.Google Scholar
- 10.Z. Cao, Q. Yin, X. Tang, and J. Sun, “Face Recognition with Learning based Descriptor”, Proceedings of Computer Vision and Pattern Recognition (CVPR), 2010.Google Scholar
- 11.P. Li, S. Prince, Y. Fu, U. Mohammed, and J. Elder. “Probabilistic Models for Inference about Identity”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012.Google Scholar
- 12.T. Berg and P. Belhumeur, “Tom-vs-Pete Classifiers and Identity preserving Alignment for Face Verification”, Proceedings of British Machine Vision Conference (BMVC), 2012.Google Scholar
- 13.F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A Unified Embedding for Face Recognition and Clustering”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.Google Scholar
- 14.Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deep-Face: Closing the Gap to Human Level Performance in Face Verification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.Google Scholar
- 15.A. Romero, N. Ballas, S.E. Kahou, A. Chassang, C. GattaandY. Bengio, “Fitnets: Hints for Thin Deep Nets”, arXiv:1412.6550, 2014.
- 16.J. Ba and R. Caruana, “Do deep nets really need to be deep?”, Advances in Neural Information Processing Systems 27, arXiv:1312.6184, 2014.
- 17.M.D. Zeiler, and R. Fergus, “Visualizing and Understanding Convolutional Networks”, European Conference on Computer Vision (ECCV), 2014.Google Scholar
- 18.A.M. Martinez and R. Benavente, “The AR Face Database”, CVC Technical Report #24, June 1998.Google Scholar
- 19.“The Extended Yale Face Database B”, available online: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html.