TEJAS Journal of Technologies and Humanitarian Science

Manasi Sadhankar,
Research Scholar, Department of Electronics and Computer Science, Rashtrasant Tukdoji Maharaj Nagpur University (RTMNU)Campus, Nagpur, India.

Author

Dr.Ashish Sasankar
Principal, Indraprastha New Arts, Commerce and Science College, Wardha, Affiliated to Rashtrasant Tukdoji Maharaj Nagpur University (RTMNU), India.India

Author

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Abstract:

Conventional systems with one-time authentication cannot adequately safeguard security in modern computing environments, especially when continuous user validation is essential. This paper explores the field of multimodal continuous biometric authentication using face and iris recognition for enhanced security and usability. The proposed system continuously verifies the user's identity during an active session by capturing facial and iris data in real time through a dual-camera setup. Deep convolutional neural networks are employed for robust feature extraction from both modalities, and a score-level fusion approach is applied for decision-making. The system is designed to adapt to illumination changes, partial occlusion, and spoofing attempts. Based on the expected robustness of the underlying models and prior research trends, the proposed framework is estimated to achieve high accuracy (around 97–99%) with significantly reduced error rates, thereby making it a strong candidate for real-time, secure, and user-friendly continuous authentication.

References

[1] A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4–20, 2004.
[2] Y. Meng, D. S. Wong, and K. Chen, "Continuous authentication with touch behavioral biometrics and voice on mobile phones," IEEE Access, vol. 5, pp. 16579–16591, 2017.
[3] Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "VGGFace2: A dataset for recognising faces across pose and age," in Proc. IEEE FG, 2018.
[4] A. George and S. Marcel, "Deep pixel-wise binary supervision for face presentation attack detection," in Proc. ECCV, 2018, pp. 1–19.
[5] J. Daugman, "How iris recognition works," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, 2004.
[6] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, "Image understanding for iris biometrics: A survey," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281–307, 2008.
[7] R. J. Proctor and K.-P. L. Vu, “Human factors in virtual environments for training: A review,” Human Factors, vol. 51, no. 2, pp. 289–301, Apr. 2009.
[8] L. Meng, H. Fu, and Y. Yang, “Continuous authentication by behavioral biometrics for mobile devices,” Future Generation Computer Systems, vol. 86, pp. 360–373, 2018.
[9] A. K. Jain and A. Ross, “Multibiometric systems,” Communications of the ACM, vol. 47, no. 1, pp. 34–40, Jan. 2004.
[10] D. Menotti et al., “Deep representations for iris, face, and fingerprint spoofing detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 864–879, Apr. 2015.
[11] Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “VGGFace2: A dataset for recognizing faces across pose and age,” in Proc. IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2018, pp. 67–74.
[12] A. George and S. Marcel, “Deep pixel-wise binary supervision for face presentation attack detection,” in Proc. European Conference on Computer Vision (ECCV), 2018, pp. 0–17.
[13] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: A survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281–307, May 2008.
[14] J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, Jan. 2004.
[15] A. Czajka and K. W. Bowyer, “Presentation attack detection for iris recognition: An assessment of the state of-the-art,” ACM Computing Surveys (CSUR), vol. 51, no. 4, pp. 1–35, Jul. 2018.
[16] T. Raja, R. Raghavendra, and C. Busch, “Video-based iris recognition using deep sparse filtering,” Pattern Recognition Letters, vol. 91, pp. 15–22, 2017.
[17] S. Prabhakar, S. Pankanti, A. Jain, “Biometric recognition: Security and privacy concerns,” IEEE Security & Privacy, vol. 1, no. 2, pp. 33–42, Mar.–Apr. 2003.
[18] A. Ross and A. Jain, “Information fusion in biometrics,” Pattern Recognition Letters, vol. 24, no. 13, pp. 2115–2125, Sep. 2003.
[19] A. Ross and A. Jain, “Multimodal biometrics: An overview,” in Proc. 12th European Signal Processing Conference, 2004, pp. 1221–1224.