Inter-Device Periocular Recognition Under Near-Infrared Light

Michał Włodarczyk, Paweł Krotewicz, Damian Kacperski, Wojciech Sankowski, Kamil Grabowski


Periocular biometrics is a relatively new field of

research, and only several publications on this topic can be found

in the literature. It can become a promising feature that can

be used independently or as a complement to other biometrics.

In this work, the recognition rates of periocular biometrics on

a single acquisition device and inter-device database is verified

and the impact of different image sources on the performance of

recognition algorithms is investigated. For this purpose a Near-

Infrared Light database was collected. The database contains

images taken by two acquisition devices. In order to test the

periocular biometric trait, three feature extraction methods are

chosen: Histograms of Oriented Gradients, Local Binary Patterns

and Scale Invariant Feature Transform. The fusion of these

methods is also proposed and it is tested on inter-device database.

The feasibility of applying periocular recognition as an individual

decision module for a biometric system is assessed. Experimental

results yield Equal Error Rate of 17.65% for left eye and 20.54%

for right eye using inter-device database of 640 gallery periocular

images for each eye side taken from 32 different individuals

(20 images per individual for each eye side). These results are

obtained by the optimal weighted sum fusion of the three feature

extraction methods.


Ahmed, N., Natarajan, T., Rao, K. R. (1974). Discrete cosine transform. IEEE transactions on Computers, 100(1), 90-93

Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359

Beer, T. (1981). Walsh transforms. American Journal of Physics, 49(5), 466-472

Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), 121-167

Clausiyz, D.A., Jernigany, M.E. (1996). Towards a novel approach for texture segmentation of sar sea ice imagery.

Dalal, N., Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE

Daugman, J.G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE transactions on pattern analysis and machine intelligence, 15(11), 1148-1161

Hollingsworth, K., Bowyer, K.W., Flynn, P.J. (2010, September). Identifying useful features for recognition in near-infrared periocular images. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on (pp. 1-8). IEEE

Hollingsworth, K.P., Darnell, S.S., Miller, P.E., Woodard, D.L., Bowyer, K.W., Flynn, P.J. (2012). Human and machine performance on periocular biometrics under near-infrared light and visible light. IEEE transactions on information forensics and security, 7(2), 588-601

Hurley, D.J., Nixon, M.S., Carter, J.N. (2000). A new force field transform for ear and face recognition. In Image Processing, 2000. Proceedings. 2000 International Conference on (Vol. 1, pp. 25-28). IEEE

Laws, K. I. (1980, December). Rapid texture identification. In 24th annual technical symposium (pp. 376-381). International Society for Optics and Photonics

Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110

Mallat, S.G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, 11(7), 674-693

Miller, P.E., Rawls, A.W., Pundlik, S.J., Woodard, D.L. (2010, March). Personal identification using periocular skin texture. In Proceedings of the 2010 ACM Symposium on Applied Computing (pp. 1496-1500). ACM

Miller, P.E., Lyle, J.R., Pundlik, S.J., Woodard, D.L. (2010, September). Performance evaluation of local appearance based periocular recognition. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on (pp. 1-6). IEEE

Nie, L., Kumar, A., Zhan, S. (2014, August). Periocular recognition using unsupervised convolutional RBM feature learning. In Pattern Recognition (ICPR), 2014 22nd International Conference on (pp. 399-404). IEEE

Ojala, T., Pietikäinen, M., Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59

Ojala, T., Pietikäinen, M., Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24(7), 971-987

Padole, C.N., Proenca, H. (2012, March). Periocular recognition: Analysis of performance degradation factors. In Biometrics (ICB), 2012 5th IAPR International Conference on (pp. 439-445). IEEE

Park, U., Ross, A., Jain, A.K. (2009, September). Periocular biometrics in the visible spectrum: A feasibility study. In Biometrics: Theory, Applications, and Systems, 2009. BTAS’09. IEEE 3rd International Conference on (pp. 1-6). IEEE.

Park, U., Jillela, R.R., Ross, A., Jain, A.K. (2011). Periocular biometrics in the visible spectrum. IEEE Transactions on Information Forensics and Security, 6(1), 96-106

Sklansky, J. (1982). Finding the convex hull of a simple polygon. Pattern Recognition Letters, 1(2), 79-83

Teo, C.C., Ewe, H.T. (2005). An efficient onedimensional fractal analysis for iris recognition

The OpenCV Reference Manual Release, (2014)

Wheeler, F.W., Perera, A.A., Abramovich, G., Yu, B., Tu, P.H. (2008, September). Stand-off iris recognition system. In Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on (pp. 1-7). IEEE

Woodard, D.L., Pundlik, S.J., Lyle, J.R., Miller, P.E. (2010, June). Periocular region appearance cues for biometric identification. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on (pp. 162-169). IEEE

Woodard, D.L., Pundlik, S., Miller, P., Jillela, R., Ross, A. (2010, August). On the fusion of periocular and iris biometrics in non-ideal imagery. In Pattern Recognition (ICPR), 2010 20th International Conference on (pp. 201-204). IEEE

Xu, J., Cha, M., Heyman, J.L., Venugopalan, S., Abiantun, R., Savvides, M. (2010, September). Robust local binary pattern feature sets for periocular biometric identification. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on (pp. 1-8). IEEE


  • There are currently no refbacks.