Abstract
In this paper we present bioFACE, a novel mobile application for the biometric authentication by face recognition of the users of the attendance management software provided by the Human Resources department of the State Technical University of Quevedo. This application converts the smartphones in a biometric device that allows register the workday entries and workday exits from any place inside of the university campus. The user-location is validated by the GPS coordinates using the Android Geofence API and the biometric authentication of the users (employees and professors) is carried out by face recognition performed by Microsoft Face API features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Android Geofences (2018). https://developer.android.com/training/location/geofencing. Accessed 20 May 2018
Identify faces in images (2018). https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/howtoidentifyfacesinimage. Accessed 20 May 2018
Microsoft-Face-API (2018). https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview. Accessed 20 May 2018
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: Openface: a general-purpose face recognition library with mobile applications. CMU School of Computer Science (2016)
Hadid, A., Heikkila, J., Silvén, O., Pietikainen, M.: Face and eye detection for person authentication in mobile phones. In: First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2007, pp. 101–108. IEEE (2007)
Jebara, T.S.: 3D pose estimation and normalization for face recognition. Centre for Intelligent Machines. McGill University (1995)
Kanade, T.: Picture Processing System by Computer Complex and Recognition of Human Faces (1974)
Liu, H., Xie, X., Ma, W.Y., Zhang, H.J.: Automatic browsing of large pictures on mobile devices. In: Multimedia 2003, pp. 148–155. ACM, New York (2003). https://doi.org/10.1145/957013.957045
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification, pp. 1701–1708 (2014)
Trewin, S., Swart, C., Koved, L., Martino, J., Singh, K., Ben-David, S.: Biometric authentication on a mobile device: a study of user effort, error and task disruption, ACSAC 2012, pp. 159–168. ACM, New York (2012). https://doi.org/10.1145/2420950.2420976
Veridium: Biometrics Definitions (2018). https://www.veridiumid.com/biometrics/. Accessed 20 May 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zambrano-Vega, C., Oviedo, B., Chiquito Mindiola, J., Reyes Baque, J., Moncayo Carreño, O. (2019). Mobile Biometric Authentication by Face Recognition for Attendance Management Software. In: Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., Zúñiga Sánchez, M. (eds) Technology Trends. CITT 2018. Communications in Computer and Information Science, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-05532-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-05532-5_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05531-8
Online ISBN: 978-3-030-05532-5
eBook Packages: Computer ScienceComputer Science (R0)