Fingerprint authentication is one of the most popular and accurate technology. Our project is a fingerprint attendance system that records the attendance of students based on their fingerprint matches them against the database to mark their attendance. Fingerprint-based attendance system used for ensures that there is a minimal fault in gathering attendance and also reduce cost and time required to manage attendance via paper. It reduces human effort and making the process simpler by using raspberry pi. The fingerprint system is connected to the raspberry pi. The timing is set for the fingerprint sensor for student attendance. The student put into fingerprint the message is sent to the authorized person using through an e-mail.
 I. Hussain, M. Xiao, and L. K. Rasmussen, “Erasure floor analysis of distributed lt codes,” IE
 C. Berrou, Y. Saouter, C. Douillard, S. Kerouedan, and M. Jezequel, “Designing good permutations for turbo codes: towards a single model,” in IEEE Int. Conf. on Commun.. ICC, 2014.
 Xiong Li, Jieyao Peng, Jianwei Niu, Fan Wu, Jianguo Liao, and KimKwang Raymond Choo. A robust and energy efficient authentication protocol for the industrial internet of things. IEEE Internet of Things Journal 2017.
 Xiong Li, Jianwei Niu, Md Zakirul Alam Bhuiyan, Fan Wu, Marimuthu Karuppiah , and SaruKumari. Arobusteccbasedprovable secure authentication protocol with privacy- preserving for the industrial internet of things. IEEE Transactions on Industrial Informatics 2017.
 Xiong Li and Jianwei Niu and Saru Kumari and Fan Wu and Arun Kumar Sangaiah and Kim-Kwang Raymond Choo. A three-factor anonymous authentication scheme for wireless sensor networks in the internet of things environments. Journal of Network and Computer Applications 2018.
 D. Peralta, I. Triguero, S. García, F. Herrera, and J. M. Benitez, „„DPDDFF: A dual-phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases,‟‟ Inf. Fusion, vol. 32, pp. 40–51, Nov. 2016.
 E.J.S. Luz, G.J.P. Moreira, L.S. Oliveira, W.R. Schwartz, and D. Menotti, “Learning Deep Off-the-Person Heart Biometrics Representations”, IEEE Transaction on Information Forensics and Security, vol. 13, no. 5, pp. 1258-1270, 2018.
 H. Kim and S.Y. Chun, “Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test”, IEEE Access, vol. 7, pp. 9232-9242, 2019.
 M. Cadogan, PR Interval, [Online] Available: https://litfl.com/printerval-ecg-library/, Accessed on Apr. 24, 2019
 A. Nichole and B. Rodriguez, “Artificial intelligence for the electrocardiogram”, Nature Medicine volume, vol. 25, pp. 22-23, 2019.
 A. Avati, “Evaluation Metrics”, [Online]Available: http://cs229.stanford.edu/section/evaluation_metrics.pdf, Accessed in Feb. 1, 2019.
 Y. Zhu, X. Yin, X. Jia, and J. Hu, „„Latent fingerprint segmentation based on convolutional neural networks,‟‟ in Proc. IEEE Workshop Inf. Forensics Secur. (WIFS), Dec. 2017, pp. 1–6.
 R. Cappelli, M. Ferrara, and D. Maltoni, „„Large-scale fingerprint identification on GPU,‟‟ Inf. Sci., vol. 306, pp. 1–20, Jun. 2015.
 K. E. Hoyle, N. J. Short, M. S. Hsiao, A. L. Abbott, and E. A. Fox, „„Minutiae + friction ridges = triplet-based features for determining sufficiency in fingerprints,‟‟ in Proc. IET Conf., Nov. 2011, pp. 1–6.
 D. Peralta, I. Triguero, S. García, F. Herrera, and J. M. Benitez, „„DPD-DFF: A dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases,‟‟ Inf. Fusion, vol. 32, pp. 40–51, Nov. 2016.
 J. Li, J. Feng, and C.-C. J. Kuo, „„Deep convolutional neural network for latent fingerprint enhancement,‟‟ Signal Process., Image Commun., vol. 60, pp. 52–63, Feb. 2018.
 K. Cao and A. K. Jain, „„Automated latent fingerprint recognition,‟‟ IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 4, pp. 788–800, Apr. 2019.
 K. Cao and A. K. Jain. (2018).
 A. Manickam et al., „„Score level based latent fingerprint enhancement and matching using SIFT feature,‟‟ Multimedia Tools Appl., vol. 78, no. 3, pp. 3065–3085, 2019.  FBI Biometric Identification Award 2017, Federal Bur. Invest., Dept. Justice United States America, Washington, DC, USA, Mar. 2018.
 I. E. Dror and S. A. Cole, „„The vision in „blind‟ justice: Expert perception, judgment, and visual cognition in forensic pattern recognition,‟‟ Psychonomic Bull. Rev., vol. 17, no. 2, pp. 161–167, 2010.
 D. Peralta et al., A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation,‟‟ Inf. Sci., vol. 315, pp. 67–87, Sep. 2017.
Authentication, Raspberry pi, Biometric sensor, e-mail.