Two Concurrent Convolution Neural Networks TC*CNN Model for Face Recognition Using Edge

Authors: T. Alghamdi, G. Alaghband

Abstract:

In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution neural network, edges, face recognition, support vector machine.

References:

[1] D. Trigueros, L. Meng, and M. Hartnett, “Face Recognition: From Traditional to Deep Learning Methods,” 2018.
[2] M. Z. Khan, S. Harous, S. U. Hassan, M. U. Ghani Khan, R. Iqbal, and S. Mumtaz, “Deep Unified Model for Face Recognition Based on Convolution Neural Network and Edge Computing,” IEEE Access, vol. 7, pp. 72622-72633, 2019.
[3] H. Manh and G. Alaghband, “Spatiotemporal KSVD Dictionary Learning for Online Multi-target Tracking,” 15th Conference on Computer and Robot Vision (CRV), Toronto, pp. 150-157. 2018.
[4] Y. Pan and M. Jiang, “LRR-TTK DL for face recognition,” IET Biometrics, vol. 6, no. 3, pp. 165-172, May 2017.
[5] M. Taheri, “Robust face recognition via non-linear correlation filter bank, “ IET Image Processing, vol. 12, no. 3, pp. 408-415, March 2018.
[6] P. Kamencay, M. Benco, T. Mizdos, and R. Radill, “A New Method for Face Recognition Using Convolutional Neural Network,” Digital Image Processing and Computer Graphics, vol. 15, No. 4, pp. 663-672. 2017.
[7] K. Yan, S. Huang, Y. Song, W. Liu, and N. Fan, “Face Recognition Based on Convolution Neural Network,” Proceedings of the 36th Chinese Control Conference, Dalian, China. pp. 4077-4081 July 26-28, 2017.
[8] X. Li and D. Li, "Image preprocessing study on KPCA-based face recognition,” Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981306, December 2015.
[9] Y. Jun, S. Kejia, G. Fei, and Z. Suguo. “Face biometric quality assessment via light CNN,” Pattern Recognition Letters. 2017.
[10] H. Moon, C. H. Seo, and S. B. Pan. “A face recognition system based on convolution neural network using multiple distance face,” Soft Computing, , vol. 21, no. 17, pp. 49-95, 2017.
[11] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 8, pp. 679–714, 1986.
[12] S. Qiao and J. Ma, "A Face Recognition System Based on Convolution Neural Network," 2018 Chinese Automation Congress (CAC), Xi'an, China, pp. 1923-1927. 2018.
[13] Y. YC, H. KR, and D. YS. “A new image classification model based on brain parallel interaction mechanism,” Neurocomputing. pp. 190–197, 2018.
[14] A. Georghiades, P. Belhumeur and D. Kriegman, “From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,” PAMI, 2001.
[15] A.M. Martinez and R. Benavente, ``The AR face database," CVC Tech. Report #24, 1998.
[16] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,” University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.