Abstract
In this work, we take advantage of the superiority of Spectral Graph Theory in classification application and propose a novel deep learning framework for face analysis which is called Spectral Regression Discriminant Analysis Network (SRDANet). Our SRDANet model shares the same basic architecture of Convolutional Neural Network (CNN), which comprises three basic components: convolutional filter layer, nonlinear processing layer and feature pooling layer. While it is different from traditional deep learning network that in our convolutional layer, we extract the leading eigenvectors from patches in facial image which are used as filter kernels instead of randomly initializing kernels and update them by stochastic gradient descent (SGD). And the output of all cascaded convolutional filter layers is used as the input of nonlinear processing layer. In the following nonlinear processing layer, we use hashing method for nonlinear processing. In feature pooling layer, the block-based histograms are employed to pooling output features instead of max-pooling technique. At last, the output of feature pooling layer is considered as one final feature output of our model. Different from the previous single-task research for face analysis, our proposed approach demonstrates an excellent performance in face recognition and expression recognition with 2D/3D facial images simultaneously. Extensive experiments conducted on many different face analysis databases demonstrate the efficiency of our proposed SRDANet model. Databases such as Extended Yale B, PIE, ORL are used for 2D face recognition, FRGC v2 is used for 3D face recognition and BU-3DFE is used for 3D expression recognition.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Subspace learning from image gradient orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(12), 2454–2466 (2012)
Kang, C., Liao, S., Xiang, S., Pan, C.: Local sparse discriminant analysis for robust face recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 846–853, June 2013
Ren, C.-X., Dai, D.-Q., Yan, H.: Coupled kernel embedding for low-resolution face image recognition. IEEE Transactions on Image Processing 21(8), 3770–3783 (2012)
Ramirez Rivera, A., Castillo, R., Chae, O.: Local directional number pattern for face analysis: Face and expression recognition. IEEE Transactions on Image Processing 22(5), 1740–1752 (2013)
Juefei-Xu, F., Savvides, M.: Subspace-based discrete transform encoded local binary patterns representations for robust periocular matching on nist’s face recognition grand challenge. IEEE Transactions on Image Processing 23(8), 3490–3505 (2014)
Ming, Y.: Robust regional bounding spherical descriptor for 3d face recognition and emotion analysis. Image and Vision Computing 35, 14–22 (2015)
Chu, B., Romdhani, S., Chen, L.: 3d-aided face recognition robust to expression and pose variations. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907–1914, June 2014
Ming, Y.: Rigid-area orthogonal spectral regression for efficient 3d face recognition. Neurocomputing 129, 445–457 (2014)
Ming, Y., Ruan, Q.: Robust sparse bounding sphere for 3d face recognition. Image and Vision Computing 30(8), 524–534 (2012). Special Section: Opinion Papers
Liong, V.E., Lu, J., Wang, G.: Face recognition using deep pca. In: 2013 9th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5, December 2013
Lu, C., Zhao, D., Tang, X.: Face recognition using face patch networks. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3288–3295, December 2013
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8), 1798–1828 (2013)
Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., Cun, Y.L.: Learning convolutional feature hierarchies for visual recognition. In: Advances in Neural Information Processing Systems, pp. 1090–1098 (2010)
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153, September 2009
Huang, G.B., Lee, H., Learned-Miller, E.: Learning hierarchical representations for face verification with convolutional deep belief networks. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2518–2525, June 2012
Burges, C.J.C., Platt, J.C., Jana, S.: Distortion discriminant analysis for audio fingerprinting. IEEE Transactions on Speech and Audio Processing 11(3), 165–174 (2003)
Kang, L., Kumar, J., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for document image classification. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 3168–3172, August 2014
Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q.V., Ng, A.Y.: On optimization methods for deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 265–272 (2011)
Rifai, S., Mesnil, G., Vincent, P., Muller, X., Bengio, Y., Dauphin, Y., Glorot, X.: Higher order contractive auto-encoder. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 645–660. Springer, Heidelberg (2011)
Cai, D., He, X., Han, J.: Srda: An efficient algorithm for large-scale discriminant analysis. IEEE Trans. on Knowl. and Data Eng. 20(1), 1–12 (2008)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: Pcanet: A simple deep learning baseline for image classification? arXiv preprint arXiv:1404.3606 (2014)
Lei, Z., Pietikainen, M., Li, S.Z.: Learning discriminant face descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(2), 289–302 (2014)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Cai, D., He, X., Hu, Y., Han, J., Huang, T.: Learning a spatially smooth subspace for face recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition Machine Learning (CVPR 2007) (2007)
Guo, Z., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19(6), 1657–1663 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tian, L., Fan, C., Ming, Y., Shi, J. (2015). SRDANet: An Efficient Deep Learning Algorithm for Face Analysis. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-22879-2_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22878-5
Online ISBN: 978-3-319-22879-2
eBook Packages: Computer ScienceComputer Science (R0)