Abstract
User identification is becoming more and more important for Apps on mobile devices. However, the identity recognition based on eyes, e.g., iris recognition, is rarely used on mobile devices comparing with those based on face and fingerprint due to its extra cost in hardware and complicated operations during recognition. In this article, an eye-based recognition method is designed for identity recognition on mobile devices, which can be implemented just like face recognition. In the proposed method, the eye feature is composed of the static and dynamic features, where the periocular feature extracted by deep neural network from the eye image is used as the static feature, and the motion feature of saccadic velocity is selected as the dynamic feature. The eye images can be captured by the normal camera on mobile devices just like faces, and dynamic features can provide living information to increase the difficulty of forgery. The GazeCapture dataset is used to test the proposed method, because the eye images in this dataset are captured by mobile devices during daily use. The recognition accuracy of the proposed method on the GazeCapture dataset can reach 96.87% only based on the periocular feature and can be enhanced to 97.99% when it is fused with the saccadic feature. The experiment results show that the performance of the proposed method can be comparative to that of iris recognition methods. It demonstrates that the proposed method is a practical reference for the eye-based identity recognition, and the proposed method provides one more biometric choice for mobile devices.
- Evgeniy Abdulin, Ioannis Rigas, and Oleg V. Komogortsev. 2016. Eye movement biometrics on wearable devices: What are the limits? In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 1503--1509.Google Scholar
- Ishan Bhardwaj, Narendra D. Londhe, and Sunil Kumar Kopparapu. 2019. Performance evaluation of fingerprint dynamics in machine learning and score level fusion framework. Iete Techn. Rev. 36, 2 (2019), 178--189.Google Scholar
Cross Ref
- John Daugman. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15, 11 (1993), 1148--1161.Google Scholar
Digital Library
- Akanksha Joshi, Abhishek Gangwar, Renu Sharma, and Zia Saquib. 2012. Periocular feature extraction based on LBP and DLDA. In Proceedings of the International Conference on Computer Science. 1023--1033.Google Scholar
Cross Ref
- Oleg V. Komogortsev, Alexey Karpov, and Corey Holland. 2016. Oculomotor plant characteristics: The effects of environment and stimulus. IEEE Trans. Inf. Forens. Secur. 11, 3 (2016), 621--632.Google Scholar
Cross Ref
- Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra M. Bhandarkar, Wojciech Matusik, and Antonio Torralba. 2016. Eye tracking for everyone. In Computer Vision and Pattern Recognition. 2176--2184.Google Scholar
- Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K. R. Rao, Zahid Akhtar, and Dipankar Dasgupta. 2019. Low dose abdominal CT image reconstruction: An unsupervised learning based approach. In Proceedings of the International Conference on Image Processing. 1351--1355.Google Scholar
Cross Ref
- Shiba Kuanar, Vassilis Athitsos, Nityananda Pradhan, Arabinda Mishra, and K. R. Rao. 2018. Cognitive analysis of working memory load from eeg, by a deep recurrent neural network. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 2576--2580.Google Scholar
- Chengcheng Li, Weidong Zhou, and Shasha Yuan. 2015. Iris recognition based on a novel variation of local binary pattern. Visual Comput 31, 10 (2015), 1419--1429.Google Scholar
Digital Library
- Yang Li, Wenming Zheng, Zhen Cui, and Tong Zhang. 2018. Face recognition based on recurrent regression neural network. Neurocomputing 297 (2018), 50--58.Google Scholar
- Nianfeng Liu, Man Zhang, Haiqing Li, Zhenan Sun, and Tieniu Tan. 2016. DeepIris: Learning pairwise filter bank for heterogeneous iris verification. Pattern Recogn. Lett. 82, 2 (2016), 154--161.Google Scholar
Digital Library
- Subhadeep Mukhopadhyay and Shinjini Nandi. 2018. LPiTrack: Eye movement pattern recognition algorithm and application to biometric identification. Mach. Lear. 107, 2 (2018), 313--331.Google Scholar
Digital Library
- Unsang Park, Raghavender Jillela, Arun Ross, and Anil K. Jain. 2011. Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forens. Secur. 6, 1 (2011), 96--106.Google Scholar
Digital Library
- Hugo Proenca and Joao C. Neves. 2018. Deep-PRWIS: Periocular recognition without the iris and sclera using deep learning frameworks. IEEE Trans. Inf. Forens. Secur. 13, 4 (2018), 888--896.Google Scholar
Cross Ref
- Ioannis Rigas and Oleg V. Komogortsev. 2014. Biometric recognition via probabilistic spatial projection of eye movement trajectories in dynamic visual environments. IEEE Trans. Inf. Forens. Secur. 9, 10 (2014), 1743--1754.Google Scholar
Digital Library
- Kaushik Roy, Prabir Bhattacharya, and R. C. Debnath. 2007. Multi-class SVM based iris recognition. In Proceedings of the 10th International Conference on Computer and Information Technology. 1--6.Google Scholar
- Huiru Shao, Jing Li, Wenbo Wan, Huaxiang Zhang, and Jiande Sun. 2020. Saccadic trajectory-based identity authentication. Multimedia Tools Appl. 79, 7 (2020), 1--15.Google Scholar
Cross Ref
- Jiande Sun, Yufei Wang, Jing Li, Wenbo Wan, De Cheng, and Huaxiang Zhang. 2018. View-invariant gait recognition based on kinect skeleton feature. Multimedia Tools Appl. 77, 19 (2018), 24909--24935.Google Scholar
Digital Library
- Zhenan Sun and Tieniu Tan. 2009. Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31, 12 (2009), 2211--2226.Google Scholar
Digital Library
- Leslie Ching Ow Tiong, Andrew Beng Jin Teoh, and Yunli Lee. 2019. Periocular recognition in the wild with orthogonal combination of local binary coded pattern in dual-stream convolutional neural network. In Proceedings of the International Conference on Biometrics.Google Scholar
Cross Ref
- Yufei Wang, Jiande Sun, Jing Li, and Dong Zhao. 2016. Gait recognition based on 3D skeleton joints captured by kinect. In Proceedings of the International Conference on Image Processing, 3151--3155.Google Scholar
Cross Ref
- Zi Wang, Chengcheng Li, Huiru Shao, and Jiande Sun. 2018. Eye recognition with mixed convolutional and residual network (MiCoRe-Net). IEEE Access 6 (2018), 17905--17912.Google Scholar
Cross Ref
- Jie Zhang and Robert B. Fisher. 2019. 3D visual passcode: Speech-driven 3D facial dynamics for behaviometrics. Signal Process. 160 (2019), 164--177.Google Scholar
Digital Library
- Qi Zhang, Haiqing Li, Zhenan Sun, and Tieniu Tan. 2018. Deep feature fusion for iris and periocular biometrics on mobile devices. IEEE Trans. Inf. Forens. Secur. 13, 11 (2018), 2897--2912.Google Scholar
Cross Ref
- Zijing Zhao and Ajay Kumar. 2017. Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network. IEEE Trans. Inf. Forens. Secur. 12, 5 (2017), 1017--1030.Google Scholar
Cross Ref
- Zijing Zhao and Ajay Kumar. 2017. Towards more accurate iris recognition using deeply learned spatially corresponding features. In Proceedings of the International Conference on Computer Vision. 3829--3838.Google Scholar
Cross Ref
Index Terms
Eye-based Recognition for User Identification on Mobile Devices
Recommendations
Selective generation of Gabor features for fast face recognition on mobile devices
In this paper, we propose a robust face recognition method to provide fast response on a mobile device by selectively generating Gabor features. The Gabor filter has been popularly used in face recognition to improve recognition performance. Since the ...
Eye gesture recognition on portable devices
UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous ComputingHand-held portable devices have received only little attention as a platform in the eye tracking community so far. This is mainly due to their -- until recently -- limited sensing capabilities and processing power. In this work-in-progress paper we ...
Eye Detection and Recognition in the Fatigue Warning System
ICINIS '10: Proceedings of the 2010 Third International Conference on Intelligent Networks and Intelligent SystemsFor a driver fatigue warning system, one of the most important problems to solve is eye detectiong and recognition. The blink information was used to preliminary eye location. Modified Susan operator of eye inner corner extraction to accurate eye ...






Comments