Abstract
Face image processing has become one of the fields of computer vision in processing computerized image patterns; the face becomes one of the vital biometrics that stores essential information used in predicting the characteristics of a person. Biometric techniques with facial recognition systems are now required in various fields, including business, one of which is the attendance marking system that is a crucial repetitive transaction requirement because it relates to employee productivity. In terms of ethics, attendance recording using a person’s face has many benefits; one of them is removing the necessity to make direct contact with the scanning device. Before doing face recognition, one of the preprocessing stages is face detection as an effort to find the existence of a face image consisting of eyes, nose, mouth, and other facial features. This research employed Viola-Jones method for face detection, Gabor Wavelet for feature extraction, and Template Matching. Two scenarios are applied for attendance recording, individual face recording, and group face recording where several faces are captured simultaneously, and each face is extracted and recognized. For Individual attendance recognition, this research achieved an accuracy of 75%, recall 64%, and precision of 88%. The better result is shown on simultaneous/group face recognition, and the research achieved 88% accuracy, 75% of recall, and 97% of the precision score.
Similar content being viewed by others
Change history
03 March 2021
A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10746-x
References
Alphonse AS, Starvin MS (2019) A novel maximum and minimum response-based Gabor (MMRG) feature extraction method for facial expression recognition. Multimed Tools Appl 78(16):23369–23397. https://doi.org/10.1007/s11042-019-7646-9
Banerjee SP, Woodard D (2012) Biometric authentication and identification using keystroke dynamics: a survey. J Pattern Recognit Res 7(1):116–139. https://doi.org/10.13176/11.427
Barina D (2016) Gabor wavelets in image processing. CoRR abs/1602.0(2):1–6. Available: http://arxiv.org/abs/1602.03308
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. https://doi.org/10.1109/34.598228
Bhattacharyya D, Ranjan R, FAA, Choi M (2009) Biometric authentication: A review. 2(3):13–28
Boka A, Morris B (2019) Person recognition for access logging. IEEE 9th Annu Comput Commun Work Conf CCWC. 2019, pp. 933–936, https://doi.org/10.1109/CCWC.2019.8666483.
Brunelli R (2009) Template matching techniques in computer vision: theory and practice. WILEY
Chatterjee D, Chandran S (2016, 2017) Comparative study of camshift and KLT algorithms for real time face detection and tracking applications. Proc. - 2016 2nd IEEE Int. Conf. Res. Comput. Intell. Commun. Networks, ICRCICN:62–65. https://doi.org/10.1109/ICRCICN.2016.7813552
Christou N, Kanojiya N (2019) Human facial expression recognition with convolution neural networks. Third International Congress on Information and Communication Technology:539–545. https://doi.org/10.1007/978-981-13-1165-9_49
Crisan S (2017) A novel perspective on hand vein patterns for biometric recognition: problems, challenges, and implementations. In: Jiang R, Al-maadeed S, Bouridane A, Crookes PD, Beghdadi A (eds) Biometric security and privacy: Opportunities & challenges in the big data era. Springer International Publishing, Cham, pp 21–49. https://doi.org/10.1007/978-3-319-47301-7_2
Dhall A, Goecke R, Lucey S, Gedeon T (2012) Collecting large, richly annotated facial-expression databases from movies. IEEE Multimed (3):34–41
Gao Q (2012) Biometric authentication in smart grid. Int Energy Sustain Conf IESC 2012. https://doi.org/10.1109/IESC.2012.6217197
Jain A, Hong L, Sharath P (2000) Biometric identification. Commun ACM 43(2): 90–98. https://doi.org/10.1145/328236.328110
Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans CIRCUITS Syst VIDEO Technol 14(1):4–20. https://doi.org/10.1109/TCSVT.2003.818349
Kim Y, Toh KA (2007) A method to enhance face biometric security. IEEE Conf Biometrics Theory, Appl Syst BTAS’07, no. i, https://doi.org/10.1109/BTAS.2007.4401913.
Kim KH, Lee S, Shim JB, Chang KH, Yang DS, Yoon WS, Park YJ, Kim CY, Cao YJ (2017) A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: a preliminary study. J Korean Phys Soc 71(4):231–237. https://doi.org/10.3938/jkps.71.231
Kristian Y, Purnama KE, Sutanto EH, Zaman L, Setiawan EI, Purnomo MH (2018) Klasifikasi Nyeri pada Video Ekspresi Wajah Bayi Menggunakan DCNN Autoencoder dan LSTM. JNTETI 7(3):308–316. https://doi.org/10.22146/jnteti.v7i3.440
Kumar PM, Gandhi U, Varatharajan R, Manogaran G Jr, Vadivel T (2019) Intelligent face recognition and navigation system using neural learning for smart security in internet of things. Cluster Comput 22:7733–7744. https://doi.org/10.1007/s10586-017-1323-4
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Nedjah N, Wyant RS, Mourelle LM, Gupta BB (2017) Efficient yet robust biometric Iris matching on smart cards for data high security and privacy. Futur Gener Comput Syst 76:18–32. https://doi.org/10.1016/j.future.2017.05.008
Nyein T, Oo AN (2019) University classroom attendance system using facenet and support vector machine. Int Conf Adv Inf Technol ICAIT 2019:171–176. https://doi.org/10.1109/AITC.2019.8921316
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987. https://doi.org/10.1109/TPAMI.2002.1017623
Pato JN, Millett LI (2010) Biometric recognition: Challenges and opportunities. Washington (DC): www.nap.edu
Patro KK, Reddi SPR, Khalelulla SKE, Kumar PR, Shankar K (2020) ECG data optimization for biometric human recognition using statistical distributed machine learning algorithm. J Supercomput 76(2):858–875
Praveenbalaji D, Srinivas R, Roopa S, Suresh M, Gayathri A (2020) ID photo verification by face recognition. 6th Int Conf Adv Comput Commun Syst ICACCS 2020:1449–1453. https://doi.org/10.1109/ICACCS48705.2020.9074246
Sameem MSI, Qasim T, Bakhat K (2017) Real time recognition of human faces, ICOSST 2016–2016. Int Conf Open Source Syst Technol Proc:62–65. https://doi.org/10.1109/ICOSST.2016.7838578
Shoba VBT, Sam IS (2020) A hybrid features extraction on face for efficient face recognition. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08997-1
Taloba AI, Sewisy AA, Dawood YA (2019) Accuracy enhancement scaling factor of viola-jones using genetic algorithms. ICENCO 2018 - 14th Int Comput Eng Conf Secur Smart Soc:209–212. https://doi.org/10.1109/ICENCO.2018.8636121
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Accepted Conference On Computer Vision And Pattern Recognition, pp 511–518. https://doi.org/10.1109/CVPR.2001.990517
Wati DAR, Abadianto D (2018) Design of face detection and recognition system for smart home security application. Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-Janua, pp. 342–347, https://doi.org/10.1109/ICITISEE.2017.8285524.
Wati V, Kusrini, Al Fatta H (2019) Real time face expression classification using convolutional neural network algorithm. Int Conf Inf Commun Technol ICOIACT 2019, no. Clm, pp. 497–501, https://doi.org/10.1109/ICOIACT46704.2019.8938521.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised: The corresponding author was incorrect.
Rights and permissions
About this article
Cite this article
Wati, V., Kusrini, K., Al Fatta, H. et al. Security of facial biometric authentication for attendance system. Multimed Tools Appl 80, 23625–23646 (2021). https://doi.org/10.1007/s11042-020-10246-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10246-4