Simultaneous Iris and Periocular Region Detection Using Coarse Annotations
In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations can be used in recognition systems based on the iris and the periocular region, given the much smaller engineering effort required to manually annotate the training images. We manually made coarse annotations of the iris and periocular regions (â‰ˆ 122K images from visible (VIS)spectrum and â‰ˆ 38K images from near-infrared (NIR) spectrum). The iris annotations in the NIR databases were generated semi-automatically, applying an iris segmentation CNN, and thenperforming a manual correction. These annotations were made for 11 well-known public databases (3 NIR and 8 VIS) designed for the iris-based recognition problem, and are publicly availableto the research community. Experimenting our proposal on these databases, we highlight two results. First, the Faster R-CNN + Feature Pyramid Network (FPN) model reported an Intersectionover Union (IoU) higher than the YoloV2 model (91.86% vs 85.30%). Second, the detection of the iris and periocular regions being performed simultaneously is as accurate as performedseparately, with lower computational cost, i.e., two tasks were carried out at the cost of one.
K. W. Bowyer, M. J. Burge, Handbook of iris recognition, 2016.
A. Das, U. Pal, M. A. F. Ballester, M. Blumenstein, "Sclera recognition using dense-SIFT", International Conference on Intelligent Systems Design and Applications, pp. 74-Dec 2013.
D. Menotti, G. Chiachia, A. Pinto, W. R. Schwartz, H. Pedrini, A. X. Falcão, A. Rocha, "Deep representations for iris face and fingerprint spoofing detection", IEEE Transactions on Information Forensics and Security, vol. no. 4, pp. 864-8April 2015.
A. Das, U. Pal, M. A. F. Ballester, M. Blumenstein, "Multi-angle based lively sclera biometrics at a distance", IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 22-Dec 2014.
I. Nigam, M. Vatsa, R. Singh, "Ocular biometrics: A survey of modalities and fusion approaches", Information Fusion, vol. pp. 1-2015.
D. R. Lucio, R. Laroca, E. Severo, A. S. Britto, D. Menotti, "Fully convolutional networks and generative adversarial networks applied to sclera segmentation", IEEE International Conference on Biometrics Theory Applications and Systems (BTAS), pp. 1-7, Oct 2018.
R. Hill, Apparatus and method for identifying individuals through their retinal vasculature patterns, 1978.
U. Park, A. Ross, A. K. Jain, "Periocular biometrics in the visible spectrum: A feasibility study", IEEE International Conference on Biometrics: Theory Applications and Systems, pp. 1-6, Sep. 2009.
Yong Zhu, Tieniu Tan, Yunhong Wang, "Biometric personal identification based on iris patterns", International Conference on Pattern Recognition (ICPR), vol. 2, pp. 801-804, Sep. 2000.
A. K. Jain, R. Bolle, S. Pankanti, Biometrics Personal Identification in Networked Society, Kluwer Academic Publishers, 1998.
C. Tan, A. Kumar, "Human identification from at-a-distance images by simultaneously exploiting iris and periocular features", International Conference on Pattern Recognition, pp. 553-5Nov 2012.
K. I. Chang, K. W. Bowyer, P. J. Flynn, X. Chen, "Multi-biometrics using facial appearance shape and temperature", International Conference on Automatic Face and Gesture Recognition, pp. 43-2004.
L. Xiao, Z. Sun, T. Tan, "Fusion of iris and periocular biometrics for cross-sensor identification", Biometric Recognition, pp. 202-209, 2012.
M. D. Marsico, M. Nappi, H. Proença, "Results from MICHE II - Mobile Iris CHallenge Evaluation II", Pattern Recognition Letters, vol. 91, pp. 3-2017.
J. Redmon, A. Farhadi, "YOLO9000: Better faster stronger", IEEE Conference on Computer Vision and Pattern Recognition (CPVR), pp. 6517-652017.
S. Ren, K. He, R. Girshick, J. Sun, "Faster R-CNN: Towards realtime object detection with region proposal networks", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. no. 6, pp. 1137-11June 2017.
Y. Ding, Q. Tao, L. Wang, D. Li, M. Zhang, "Image-based localisation using shared-information double stream hourglass networks", Electronics Letters, vol. no. 8, pp. 496-498, 2018.
K. E. Ko, K. B. Sim, "Real-time Object entity detection system for smart surveillance application", Electronics Letters, vol. no. pp. 1304-1306, 2017.
R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, D. Menotti, "A robust real-time automatic license plate recognition based on the YOLO detector", International Joint Conference on Neural Networks (IJCNN), pp. 1-July 2018.
J. G. Daugman, "High confidence visual recognition of persons by a test of statistical independence", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. no. pp. 1148-11Nov 1993.
J. Daugman, "How iris recognition works", IEEE Transactions on Circuits and Systems for Video Technology, vol. pp. 21-2004.
L. A. Zanlorensi, E. Luz, R. Laroca, A. S. Britto, L. S. Oliveira, D. Menotti, "The impact of preprocessing on deep representations for iris recognition on unconstrained environments", Conference on Graphics Patterns and Images (SIBGRAPI), pp. 289-296, Oct 2018.
E. Luz, G. Moreira, L. A. Z., D. Menotti, "Deep periocular representation aiming video surveillance", Pattern Recognition Letters, vol. 1pp. 2-2018.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, B. Schiele, "The cityscapes dataset for semantic urban scene understanding", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213-32June 2016.
C. S. Bezerra, R. Laroca, D. R. Lucio, E. Severo, L. F. Oliveira, A. S. Britto, D. Menotti, "Robust iris segmentation based on fully convolutional networks and generative adversarial networks", Conference on Graphics Patterns and Images, pp. 281-288, Oct 2018.
P. H. Silva, E. Luz, L. A. Zanlorensi, D. Menotti, G. Moreira, "Multimodal feature level fusion based on particle swarm optimization with deep transfer learning", IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, July 2018.
N. Aginako, J. M. Martínez-Otzeta, I. Rodriguez, E. Lazkano, B. Sierra, "Machine learning approach to dissimilarity computation: Iris matching", International Conference on Pattern Recognition (ICPR), pp. 170-1Dec 2016.
A. Deshpande, S. Dubey, H. Shaligram, A. Potnis, S. Chavan, "Iris recognition system using block based approach with DWT and DCT", IEEE Anual India Conference, pp. 1-5, Dec 2014.
J. L. G. Rodríguez, Y. D. Rubio, "A new method for iris pupil contour delimitation and its application in iris texture parameter estimation", Progress in Pattern Recognition Image Analysis and Applications. Springer Berlin Heidelberg, pp. 631-62005.
W. Zhang, Y. D. Ma, "A new approach for iris localization based on an improved level set method", International Computer Conference on Wavelet Actiev Media Technology and Information Processing, pp. 309-32014.
Y. Alvarez-Betancourt, M. Garcia-Silvente, "A fast iris location based on aggregating gradient approximation using QMA-OWA operator", International Conference on Fuzzy Systems, pp. 1-8, July 2010.
L. Zhou, Y. Ma, J. Lian, Z. Wang, "A new effective algorithm for iris location", IEEE ROBIO, pp. 1790-1795, 2013.
G. Läthén, T. Andersson, R. Lenz, M. Borga, "Momentum based optimization methods for level set segmentation", Scale Space and Variational Methods in Computer Vision. Springer Berlin Heidelberg, pp. 124-12009.
Wang Zhejin, Y. Feng, Qinqin Tao, "Momentum based level set segmentation for complex phase change thermography sequence", International Conference on Computer Application and System Modeling, vol. pp. 257-2Oct 2010.
Casia version 2 database., 2004, [online] Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=2.
Casia version 4 database, 20[online] Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=4.
J. Peláez, J. Doña, "A majority model in group decision making using QMA-OWA operators", International Journal of Intelligent Systems, vol. no. 2, pp. 193-208, 2006.
Zhu Yu, Wang Cui, "A rapid iris location algorithm based on embedded", International Conference on Computer Science and Information Processing (CSIP), pp. 233-2Aug 2012.
L. Su, J. Wu, Q. Li, Z. Liu, "Iris location based on regional property and iterative searching", IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1064-10Aug 2017.
C. Chen, A. Ross, "A multi-task convolutional neural network for joint iris detection and presentation attack detection", IEEE Winter Applications of Computer Vision Workshops, pp. 44-March 2018.
E. Severo, R. Laroca, C. S. Bezerra, L. A. Zanlorensi, D. Weingaertner, G. Moreira, D. Menotti, "A benchmark for iris location and a deep learning detector evaluation", International Joint Conference on Neural Networks (IJCNN), pp. 1-7, July 2018.
C. Wang, Y. Zhu, Y. Liu, R. He, Z. Sun, "Joint iris segmentation and localization using deep multi-task learning framework", CoRR, 20[online] Available: http://arxiv.org/abs/1901.11195.
U. Park, R. R. Jillela, A. Ross, A. K. Jain, "Periocular biometrics in the visible spectrum", IEEE Transactions on Information Forensics and Security, vol. 6, no. 1, pp. 96-106, 2011.
P. Viola, M. J. Jones, "Robust Real-Time Face Detection", International Journal of Computer Vision, no. 2, pp. 137-12004.
F. Juefei-Xu, M. Savvides, "Unconstrained periocular biometric acquisition and recognition using COTS PTZ camera for uncooperative and non-cooperative subjects", IEEE Workshop on the Applications of Computer Vision (WACV), pp. 201-208, Jan 2012.
G. Mahalingam, K. Ricanek, A. M. Albert, "Investigating the periocular-based face recognition across gender transformation", IEEE Transactions on Information Forensics and Security, vol. 9, no. pp. 2180-2192, 2014.
T. H. N. Le, U. Prabhu, M. Savvides, "A novel eyebrow segmentation and eyebrow shape-based identification", IEEE International Joint Conference on Biometrics (IJCB), pp. 1-8, 2014.
H. Proença, J. C. Neves, G. Santos, "Segmenting the periocular region using a hierarchical graphical model fed by texture/shape information and geometrical constraints", IEEE International Joint Conference on Biometrics (IJCB), pp. 1-7, 2014.
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, "You only look once: Unified real-time object detection", IEEE Conference on Computer Vision and Pattern Recognition (CPVR), pp. 779-788, 2016.
T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, S. Belongie, "Feature pyramid networks for object detection", IEEE Conference on Computer Vision and Pattern Recognition, pp. 936-9July 2017.
A. Sequeira et al., "Cross-eyed - cross-spectral iris/periocular recognition database and competition", International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1-5, Sep. 2016.
G. Santos, E. Grancho, M. V. Bernardo, P. T. Fiadeiro, "Fusing iris and periocular information for cross-sensor recognition", Pattern Recognition Letters, vol. pp. 52-2015.
M. de Marsico, M. Nappi, D. Riccio, H. Wechsler, "Mobile Iris Challenge Evaluation (MICHE)-I biometric iris dataset and protocols", Pattern Recognition Letters, vol. pp. 17-2015.
A. F. Sequeira, J. C. Monteiro, A. Rebelo, H. P. Oliveira, "MobBIO: A Multimodal Database Captured with a Portable Handheld Device", International Conference on Computer Vision Theory and Applications (VISAPP), vol. 3, pp. 133-12014.
H. Proenca, S. Filipe, R. Santos, J. Oliveira, L. A. Alexandre, "The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. no. 8, pp. 1529-152010.
P. R. Nalla, A. Kumar, "Toward more accurate iris recognition using cross-spectral matching", IEEE Transactions on Image Processing, vol. no. 1, pp. 208-22017.
A. Rattani, R. Derakhshani, S. K. Saripalle, V. Gottemukkula, "ICIP 2016 competition on mobile ocular biometric recognition", IEEE International Conference on Image Processing, pp. 320-32016.
M. Everingham, L. van Gool, C. K. I. Williams, J. Winn, A. Zisserman, "The pascal visual object classes (VOC) challenge", International Journal of Computer Vision, vol. 88, no. 2, pp. 303-3Jun 2010.
F. Wilcoxon, S. Katti, R. A. Wilcox, "Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test", Selected tables in mathematical statistics, vol. 1, pp. 171-21970.