International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.15, No.2, Apr. 2023

Patch Based Sclera and Periocular Biometrics Using Deep Learning

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V. Sandhya, Nagaratna P. Hegde

Index Terms



Biometric authentication has become an essential security aspect in today's digitized world. As limitations of the Unimodal biometric system increased, the need for multimodal biometric has become more popular.  More research has been done on multimodal biometric systems for the past decade. sclera and periocular biometrics have gained more attention. The segmentation of sclera is a complex task as there is a chance of losing some of the features of sclera vessel patterns. In this paper we proposed a patch-based sclera and periocular segmentation. Experiments was conducted on sclera patches, periocular patches and sclera-periocular patches. These sclera and periocular patches are trained using deep learning neural networks. The deep learning network CNN is applied individually for sclera and periocular patches, on a combination of three Data set. The data set has images with occlusions and spectacles. The accuracy of the proposed sclera-periocular patches is 97.3%. The performance of the proposed patch-based system is better than the traditional segmentation methods. 

Cite This Paper

V. Sandhya, Nagaratna P. Hegde, "Patch Based Sclera and Periocular Biometrics Using Deep Learning", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.2, pp.15-30, 2023. DOI:10.5815/ijcnis.2023.02.02


[1]P. Kao, S. Shailja, J. Jiang, A. Zhang, A. Khan, J. W. Chen and B. S. Manjunath, “Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information”, Frontiers of Neuro Science, 2020.

[2]S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” arXiv.2001.05566[cs.CV], 2020.

[3]S. Gayathri, P. Varun, P. Gopi, and P. Palanisamy, “A lightweight CNN for Diabetic Retinopathy classification from fundus images,” Biomedical Signal Processing and Control, vol. 62, pp.102-115, 2020.

[4]B. Taibou, M. Hidane, J. Olivier, H. Cardot, “From Patch to Image Segmentation using Fully Convolutional Networks - Application to Retinal Images”, Computerized Medical Image and Graphics (CMIG), 2019.

[5]Md. Anwar Hossain and Md. Shahriar Alam Sajib, “Classification of Image using Convolutional Neural Network (CNN)”, Global Journal of Computer Science and Technology: D Neural & Artificial Intelligence, vol. 9, pp.1-7,2019

[6]K. Punam, and K. R. Seeja, “Periocular biometrics: A survey”, Journal of King Saud University – Computer and Information Sciences, pp. 1-12, 2019 

[7]M. Hesam, W. Jia, X. He, P. Kennedy, “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges”, Journal of Digital Imaging, 2019.

[8]H. Yo-Ping, H. Basanta, “Bird Image Retrieval and Recognition Using a Deep Learning Platform,” IEEE, Access, vol. 7, pp-66980-66989, 42019

[9]C. Leslie, O. Tiong, Y. Lee, A. Beng J. Teoh, “Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN,” Applied Sciences, vol. 9, no 13, 2019.

[10]M. Hui, and Y. Lu, “Multimodal Biometrics based on Convolution Neural Networks by Two-Layer Fusion”, 12th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics(CISP-BMEI), pp.1-6, 2019.

[11]R. Kaushiki, D. Banik, D. Bhattacharjee, M. Nasipuri, “Patch-based system for Classification of Breast Histology images using deep learning”, Computerized Medical Imaging and Graphics, 2018.

[12]P. Hugo¸ and J. C. Neves, “Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning rameworks”, IEEE Transactions on Information Forensics and Security, vol. 13, pp. 888-896, 2018.

[13]Peter Rot, Ziga Emersic, Vitomir Struc, Peter. Deep Multi-class Eye Segmentation for ocular Biometrics, IEEE International Work Conference on Bioinspired Intelligence, pp.1-8,July 2018

[14]D. Wei, H. Zhou, X. Dongu, “A New Sclera Segmentation and Vessel Extraction Method for Sclera Recognition”, International conference on communication software and Networks (ICCSN), pp. 552-556, 2018.

[15]S. Atharva, L. Xiuwen, X. Yang, D. Shi, “A patch-based convolutional neural network for remote sensing image classification”, Neural Networks, vol. 8, 2017.

[16]M. Roey, J. Goldberger and H. Greenspan, “Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI”, International Journal of Biomedical Imaging, vol. 24, 2016.

[17]H. Le, D. Samaras, T. M. Kurc, Y. Gao, E. James Davis, and J. H. Saltz, “Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification”, CVF.

[18]G. H. Chen, D. Shah, and P. Golland, “A Latent Source Model for Patch-Based Image Segmentation”, Med Image Comput Comput Assist Interv. 2016.

[19]Yu Li-jie, Li De-sheng, Zhou Guan-ling,” Automatic Image Segmentation Base on Human Color Perceptions”, International Journal of Image, Graphics and Signal Processing, 2009, 1, 25-32

[20]Z. Lei, X. Wang, N. Penwarden, and Q. Ji, “An Image Segmentation Framework Based on Patch Segmentation Fusion”, International Conference on Pattern Recognition, 2006.

[21]Shiv Gehlot, John Deva Kumar,” The Image Segmentation Techniques”, International Journal of Image, Graphics and Signal Processing 2017, 2, 9-18.