Journal of Hospitality Application and Research

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Biometric Face Recognition: Application of Neural Networks and Fuzzy Control in Hospitality Industry


Affiliations
1 B.Tech, Vellore Institute of Technology, Vellore, Tamil Nadu, India
2 Associate Professor, School of Business Management, NMIMS University, Mumbai, Maharashtra, India
3 B Tech, Vellore Institute of Technology, Vellore, Tamil Nadu, India
     

   Subscribe/Renew Journal


The pattern detection device for biometric identification, which is discussed in the following paper, made use of mathematical modelling and descriptive statistics together with exploratory factor analysis i.e., Principal Component Analysis on the point of a function extraction technique. The proposed prestige gadget consisted of androgenic hair for the identification of biometric features with four hundred photographs for each database. A total of four-hundred pictures were gathered from each database. It was taken from a total of 25 respondents and sixteen snapshots from each respondent from hospitality industry. Performed with the highest accuracy, the system utilized a histogram equation with 2-fold cross-recognition, seventy-six. 68% of the average precision for the facial database and 19% mean accuracy for the androgenic hair database. Both means of accuracy are achieved using the 90 maximum large eigenvalues and their complementary eigenvectors within the principal component analysis attribute extraction technique.

Keywords

Neural Network, Fuzzy Control, Principal Component Analysis, Factor Analysis, Artificial Intelligence, Biometric, Hospitality Industry
Subscription Login to verify subscription
User
Notifications
Font Size


  • Bouhou, L. H., Ayachi, R., Baslam, M., & Oukessou, M. (2016). Face detection in a mixed-subject document. International Journal of Electrical and Computer Engineering (IJECE), 6, 2828-2835. doi:10.11591/ijece.v6i6.12725

  • Boualleg, H., Bencheriet, C., & Tebbikh, H. (2006). Automatic face recognition using neural network-PCA. 2006 2nd International Conference on Information & Communication Technologies, pp. 1920-1925. Damascus. doi:10.1109/ICTTA.2006.1684683

  • Chang, K. I., Bowyer, K. W., & Flynn, P. J. (2005). An evaluation of multimodal 2d + 3d face biometrics. IEEE Trans. Pattern Anal. Mach. Intell., 27(4), 619-624.

  • Chatfield, C., & Collins, A. J. (2018). Introduction to multivariate analysis. doi: 10.1201/9780203749999

  • Ćuk, A., Miljković, B., Todorović, M., Ivanović, A., & Živković, M. (2019). Application for student attendance based on face recognition. Proceedings of the International Scientific Conference – Sinteza.

  • http://www.rakinda.com/en/productdetail/83/132/207.html?gclid=EAIaIQobChMIg- vIzC6QIVRg4rCh2BnAMbEAAYAS AAEgJiS_D_BwE

  • https://link.springer.com/referenceworkentry/10.1007%2F978-0- 387-73003-5_84

  • https://us.norton.com/internetsecurity-iot-how-facial-recognition-software-works.html

  • https://us.norton.com/internetsecurity-iot-how-facial-recognition-software-works.html

  • https://www.jumio.com/facial-recognition-vs-facial-authentication/

  • https:/ /www.milestonesys.com/community/marketplace/start-exploring/?index=0&sortby=1&f usage=Bio&gclid=EAIaIQobChMIg-__vIzC6QIVRg4rCh2 BnAMbEAAYAyAAEgIXF_D_BwE

  • https://www.ncbi.nlm.nih.gov/books/NBK219892/

  • https://www.researchgate.net/figure/Categories-of-factors-affectingfacerecognition-accuracy_fig1_337446642

  • Jalled, F., & Voronkov, I. (2016). Object detection using Image processing. ArXiv, abs/1611.07791.

  • Jolliffe, I. T. (2002). Principal component analysis. Springer Science & Business Media.

  • Kim, J., & Mueller, C. W. (1978). Factor analysis: Statistical methods and practical issues. Sage Publications, Beverly Hills, CA.

  • Neth, D., & Martinez, A. M. (2009). Emotion perception in emotionless face images suggests a norm based representation. J. Vis., 9(1), 1-11.

  • Sirovich, L., & Kirby, M. (1986). A low dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A., 4(3), 519-524.

  • Suhr, J., Koratkar, N., Keblinski, P., & Ajayan, P. (2005). Viscoelasticity in carbon nanotube composites. Nature Mater, 4, 134-137.

  • Shivakumar, G., & Vijaya, P. A. (2013). Chapter 31 an improved artificial neural network based emotion classification system for expressive facial images. Springer Science and Business Media LLC.

  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham heights, MA: Allyn & Bacon. ISBN 0-321-05677-9

  • Teoh, A. B. J., Ngo, D., & Goh, A. (2004). Personalised cryptographic key generation based on facehasshing. Comput. Security, 23(7), 606-614.

  • von Storch, H., & Zwiers, F. W. (1999). Statistical analysis in climate research (p. 484). Cambridge University Press.

  • Wilks, D. S. (1995). Statistical methods in the atmospheric sciences (p. 467). Academic Press, San Diego.

  • Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 24, 34-58.


Abstract Views: 136

PDF Views: 0




  • Biometric Face Recognition: Application of Neural Networks and Fuzzy Control in Hospitality Industry

Abstract Views: 136  |  PDF Views: 0

Authors

Harshit Sharma
B.Tech, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Ashu Sharma
Associate Professor, School of Business Management, NMIMS University, Mumbai, Maharashtra, India
Dhruv Chouhan
B Tech, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract


The pattern detection device for biometric identification, which is discussed in the following paper, made use of mathematical modelling and descriptive statistics together with exploratory factor analysis i.e., Principal Component Analysis on the point of a function extraction technique. The proposed prestige gadget consisted of androgenic hair for the identification of biometric features with four hundred photographs for each database. A total of four-hundred pictures were gathered from each database. It was taken from a total of 25 respondents and sixteen snapshots from each respondent from hospitality industry. Performed with the highest accuracy, the system utilized a histogram equation with 2-fold cross-recognition, seventy-six. 68% of the average precision for the facial database and 19% mean accuracy for the androgenic hair database. Both means of accuracy are achieved using the 90 maximum large eigenvalues and their complementary eigenvectors within the principal component analysis attribute extraction technique.

Keywords


Neural Network, Fuzzy Control, Principal Component Analysis, Factor Analysis, Artificial Intelligence, Biometric, Hospitality Industry

References