AUTOMATED ATTENDANCE SYSTEM FOR EFFICIENT EMPLOYEE MANAGEMENT : A BIOMETRY BASED APPROACH

Authors

  • Akshat Gupta iPRIMED Education Solutions Pvt Ltd, Bengaluru, Karnataka, India
  • Anisha Kundu Deptartment of Information Technology, Xavier Institute of Social Service, Ranchi, India
  • Rik Das Deptartment of Information Technology, Xavier Institute of Social Service, Ranchi, India

Abstract

Process of maintaining attendance of employees has significant impact on the Payroll Management System of an organization. Employee payroll is a considerable share of the profit earned by an organization. Attendance system plays a crucial role to ensure adequate staffing and kindles positive employee morale resulting in delivery of expected productivity standards throughout the organization. The system attempts to embrace the accountability of employees to adhere to their workplace schedule. Several prominent organizations maintain manual attendance systems as a part of their Human Resource Management System (HRMS) which is tedious and error prone. It leads to major discrepancies in maintaining the professional attendance record of the employees which in turn adversely affects the revenue of the organization. This is because of the role of analytics in attendance system which is responsible to sanction the payroll. Most of the organizations have almost 1/4th portion of its revenue sanctioned for payroll of employees. Additionally, the stipend of interns and trainees holds 1/10th of the total cost. Hence, there is a need to develop an automated attendance management system for proficient execution of the task. Integration of biometry in an HRMS based attendance systems can help in achieving this goal of automation.

The automated system of attendance thus becomes enabled to analyze large amount of employee data per month by using automated tools and techniques. It is useful for analysing the working pattern and extracting the data of the associates almost immediately. This paper has analysed the contribution of automated biometric based attendance system for efficient management of the employee attendance. It has proposed incorporation of biometry in HRMS to reduce human error which in turn has a positive impact on the revenue of the organization.                       

Keywords:

Biometry, Automated Attendance, Analytics, Efficient Employee Management, Workplace Schedule

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References

Bilgihan, A., Karadag, E., Cobanoglu, C. & Okumus, F. (2013). Research Note: Biometric Technology Applications and Trends in Hotels. Hospitality Review, 31(2), pp 9-24.

Bilgihan, A., Karadag, E., Cobanoglu, C. & Okumus, F. (2013). Research Note: Biometric Technology Applications and Trends in Hotels. FIU Hospitality Review. 31(2), pp 1-18.

Geradts, Z. & Bijhold, J. (2002). Content Based Information Retrieval in Forensic Image Databases. Journal of Forensic Science, 47(2), pp 285-292.

Gottschlich, C. (2012). Curved-region-based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement. IEEE Transactions on Image Processing, 21(4), pp 2220-2227.

Gragnaniello, D., Poggi, G., Sansone, C., & Verdoliva, L. (2013). Fingerprint Liveness Detection Based on Weber Local Image Descriptor. 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications. 9-9 September, IEEE. Retrieved From: https://ieeexplore.ieee.org/document/6656148/authors#authors

Jackson, L.A. (2009). Biometric Technology: The Future of Identity Assurance and Authentication in the Lodging Industry. International Journal of Contemporary Hospitality Management, 21(7), pp 892-905.

Kang, B., Brewer, K.P. & Bai, B. (2007). Biometrics for Hospitality and Tourism: A New Wave of Information Technology. Hospitality Review, 25(1), pp 1-9.

Kim, J. & Bernhard, B. (2014). Factors Influencing Hotel Customers’ Intention to Use a Fingerprint System. Journal of Hospitality and Tourism Technology, 5(2), pp 98-125.

Kumar, G.S., Prabhu, M., Pandian, A.S.S., Varathan, B.J. & Selvakumar, K.N. (2014). Exploration of Seasonality in Livestock Production of Tamil Nadu. The Indian Veterinary Journal, 91(03), pp 24-27.

Lattin, T.W. (1990). Hotel Technology: Key to Survival, in: M. Quest (Ed) Horwath Book of Tourism, Horwath and Horwath. London.

Li, T.C., Wu, H.W. & Wu, T.S. (2012). The Study of Biometrics Technology Applied in Attendance Management System. In 2012 Third International Conference on Digital Manufacturing & Automation. 31st-2nd August, IEEE. Retrieved From: https://ieeexplore.ieee.org/document/6298672/authors#authors

Maroudas, L., Kyriakidou, O. & Vacharis, A. (2008). Employees' Motivation in the Luxury Hotel Industry: The Perceived Effectiveness of Human-resource Practices. Managing leisure, 13(3-4), pp 258-271.

Morosan, C. (2012a). Theoretical and Empirical Considerations of Guests’ Perceptions of Biometric Systems in Hotels: Extending the Technology Acceptance Model. Journal of Hospitality & Tourism Research, 36(1), pp 52-84.

Morosan, C. (2012b). Biometric Solutions for Today's Travel Security Problems. Journal of Hospitality and Tourism Technology, 3(3), pp 176-195.

Morosan, C. (2016). Opportunities and Challenges for Biometric Systems in Travel: A Review. Travel and Tourism Research Association: Advancing Tourism Research Globally. Retrieved From: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1686&context=ttra

Ratha, N.K., Karu, K., Chen, S. & Jain, A.K. (1996). A Real-Time Matching System for Large Fingerprint Databases. IEEE Transactions on Pattern Analysis & Machine Intelligence, 18(8), pp 799-813.

Tarare, S., Anjikar, A. & Turkar, H. (2015). Fingerprint Based Gender Classification Using DWT Transform. In 2015 International Conference on Computing Communication Control and Automation. 26th-27th February, IEEE. Retrieved From: https://ieeexplore.ieee.org/document/7155936/authors#authors

Torres, E.N. & Kline, S.F. (2006). From Satisfaction to Delight: A Model for the Hotel Industry. International Journal of Contemporary Hospitality Management, 18(4), pp 290- 301.

Zhang, Q. & Yan, H. (2004). Fingerprint Classification Based on Extraction and Analysis of Singularities and Pseudo Ridges. Pattern Recognition, 37(11), pp 2233-2243.

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Published

01-07-2019

How to Cite

Akshat Gupta, Anisha Kundu, & Rik Das. (2019). AUTOMATED ATTENDANCE SYSTEM FOR EFFICIENT EMPLOYEE MANAGEMENT : A BIOMETRY BASED APPROACH. International Journal on Recent Trends in Business and Tourism (IJRTBT), 3(3), 117-121. Retrieved from https://ejournal.lucp.net/index.php/ijrtbt/article/view/751

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