Abstract
Computerized human face matching from low quality images is an active area of research in deformable pattern recognition especially in non-cooperative security, surveillance, authentication and multi-camera tracking. In low resolution and motion-blurry face images captured from surveillance cameras, it is challenging to get good match of faces and even extracting suitable feature vectors both in classical signal/image processing based and deep learning based approaches. In the current work, we have proposed a novel low quality face image matching algorithm in the light of a neuro-visually inspired method of figure-ground segregation (NFGS). The said framework is inspired by the non-linear interaction between the classical receptive field (CRF) and its non-classical extended surround, comprising of the non-linear mean increasing and decreasing sub-units. The current work demonstrates not only better detection of low quality face images in NFGS enabled deep learning framework, but also it prescribes an efficient way of low quality face image matching addressing low contrast, low resolution and motion blur which are prime responsible factors of making image low quality. The experimental results shows the effectiveness of proposed algorithm not only quantitatively but also qualitatively in terms of psycho-visual experiments and its statistical analysis outcome.
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
References
Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, vol. 1 (1980). Proceedings of the Royal Society of London
Wang, Z., Miao, Z., Wu, Q.M.J., Wan, Y., Tang, Z.: Low-resolution face recognition: a review. Vis. Comput. 30(4), 359–386 (2014)
Yang, S., Zhang, L., He, L., Wen, Y.: Sparse low-rank component-based representation for face recognition with low-quality images. IEEE Trans. Inf. Forensics Secur. 14(1), 251–261 (2019)
Rudrani, S., Das, S.: Face recognition on low quality surveillance images, by compensating degradation. In: Kamel, M., Campilho, A. (eds.) ICIAR 2011. LNCS, vol. 6754, pp. 212–221. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21596-4_22
Haghighat, M., Abdel-Mottaleb, M.: Lower resolution face recognition in surveillance systems using discriminant correlation analysis. In: 12th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 912–917, June 2017
Karahan, S., Kilinc Yildirum, M., Kirtac, K., Rende, F.S., Butun, G., Ekenel, H.K.: How image degradations affect deep CNN-based face recognition? In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–5, September 2016
Dodge, S.F., Karam, L.J.: Understanding how image quality affects deep neural networks. CoRR, abs/1604.04004 (2016)
Ranjan, R., Patel, V.M., Chellappa, R.: HyperFace: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 121–135 (2019)
Huang, W., Yin, H.: Robust face recognition with structural binary gradient patterns. Pattern Recogn. 68, 126–140 (2017)
Zhou, Y., Liu, D., Huang, T.: Survey of face detection on low-quality images. In: 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 769–773, May 2018
Bosse, S., Maniry, D., Müller, K., Wiegand, T., Samek, W.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans. Image Process. 27(1), 206–219 (2018)
Ghosh, K., Roy, A.: Neuro-visually inspired figure-ground segregation. In: 2011 International Conference on Image Information Processing, pp. 1–6, November 2011
Das, A., Ajithkumar, N.: Engineering the perception of recognition through interactive raw primal sketch by HNFGS and CNN-MRF. In: CVIP (2017)
Das, A., Ghosh, K.: Enhancing face matching in a suitable binary environment. In: 2011 International Conference on Image Information Processing, pp. 1–6, November 2011
Ghosh, K., Bhaumik, K., Sarkar, S.: Retinomorphic image processing. Prog. Brain Res. 168, 175–91 (2007)
Rodieck, R.W., Stone, J.: Analysis of receptive fields of cat retinal ganglion cells. J. Neurophysiol. 27(1), 833–849 (1965)
Ikeda, H., Wright, J.H.: Functional organization of the periphery effect in retinal ganglion cells. J. Vis. Res. 12, 1857–1879 (1972)
Yesugade, S., Dave, P., Srivastava, S., Das, A.: Enhancing the performance of cooperative face detector by NFGS. In: Seventh International Conference on Digital Image Processing. SPIE (2015)
Face Recognition Technology (FERET) Database. https://www.nist.gov/programs-projects/face-recognition-technology-feret
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015. Conference Track Proceedings (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. CoRR, abs/1503.03832 (2015)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Das, A., Saha, P. (2019). Enhancing Low Quality Face Image Matching by Neurovisually Inspired Deep Learning. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-29891-3_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29890-6
Online ISBN: 978-3-030-29891-3
eBook Packages: Computer ScienceComputer Science (R0)