Implementation of an access control system based on bimodal biometrics with fusion of global decisions: Application to facial recognition and fingerprints

Authors

  • Bopatriciat Boluma Mangata University of Kinshasa
  • Dominique Ilunga Nakashama
  • Donatien Kadima Muamba
  • Parfum Bukanga Christian

DOI:

https://doi.org/10.24191/jcrinn.v7i2.289

Keywords:

fingerprints pattern recognition, Biometrics, Embedded System, facial recognition, access control

Abstract

Single-mode biometric systems suffer from several problems that make them unsuitable for current biometric applications that require high levels of reliability and security. These problems include the use of a single biometric trait that is prone to noise, poor capture, lack of biometric points, and deterioration of biometric input quality. In this paper, we are interested in decision fusion access control on a biometric bimodal pattern recognition system based on fingerprints and facial recognition. To realize this access control system based on facial recognition and fingerprints, we used an embedded system under Arduino, we programmed electronic systems for the automatic opening of doors without human action being. The performance evaluation of decision fusion access control on a biometric bimodal pattern recognition system is realized by means of the confusion matrix, the calculations of the evaluation parameters (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value and False Negative). From a sample of 500 individuals, 250 of whom were registered and 250 non-registered, our access control system obtained the results of 248 true positives, 2 false negative, 1 false positive and 249 true negatives which constitute our confusion matrix. However, from the set of tests performed we can conclude that by taking advantage of the fusion of these two modalities, we increase the verification performance of system as the verification performance of bimodal system (fingerprint decision fusion and facial recognition) is applied to give even better results compared to single mode systems.

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References

Benaliouche, H., & Touahria, M. (2014). Comparative study of multimodal biometric recognition by fusion of iris and fingerprint. The Scientific World Journal, 2014.

Bopatriciat Boluma Mangata & Al. (2021). Contribution of an Embedded and Biometric System in a Replicated Database for Access Control in a Multi-Entry Institution. International Journal of Science and Research (IJSR), Volume (10 Issue 3), p2-5.

Bopatriciat Boluma Mangata & al. (2022). Performance evaluation of a single access control system. Journal of research in engeneering and applied sciences. Volume (7 Issue 01), p4-6

Bowers, D. M. (2013). Access control and personal identification systems. Butterworth-Heinemann.

Caelen, O. (2017). A Bayesian interpretation of the confusion matrix. Annals of Mathematics and Artificial Intelligence, 81(3), 429-450.

Cao, K., & Jain, A. K. (2018). Automated latent fingerprint recognition. IEEE transactions on pattern analysis and machine intelligence, 41(4), 788-800.

Conger, K., Fausset, R., & Kovaleski, S. F. (2019). San Francisco bans facial recognition technology. The New York Times, 14, 1.

Douaa, M. E. C. H. T. A., & Radhwane, G. H. E. R. B. I. (2019). AUTOMATISATION DES TACHES DOMOTIQUES D’UNE MAISON A L’AIDE D’UNE CARTE ARDUINO ET LABVIEW (Doctoral dissertation, UNIVERSITE MOHAMED BOUDIAF-M’SILA).

Guizani, M., Zavala, M. L., & Funamizu, N. (2016). Assessment of endotoxin removal from reclaimed wastewater using coagulation-flocculation. Journal of Water Resource and Protection, 8(9), 855-864.

Haghighi, S., Jasemi, M., Hessabi, S., & Zolanvari, A. (2018). PyCM: Multiclass confusion matrix library in Python. Journal of Open Source Software, 3(25), 729.

Hamann, K., & Smith, R. (2019). Facial recognition technology. Criminal Justice, 34(1), 9-13.

Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: multi-label confusion matrix. IEEE Access, 10, 19083-19095.

Jacob, I. J. (2019). Capsule network based biometric recognition system. Journal of Artificial Intelligence, 1(02), 83-94.

Kaur, P., Krishan, K., Sharma, S. K., & Kanchan, T. (2020). Facial-recognition algorithms: A literature review. Medicine, Science and the Law, 60(2), 131-139.

Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., & Doulamis, N. (2021). Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies, 9(4), 81.

Mathivet, V. (2017). L'intelligence artificielle pour les développeurs: concepts et implémentations en C#. Éditions ENI.

Norman, T. L. (2011). Electronic access control. Elsevier.

Raji, I. D., & Fried, G. (2021). About face: A survey of facial recognition evaluation. arXiv preprint arXiv:2102.00813.

Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., & Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural processes, 148, 56-62.

Wirotius, M. (2005). Authentification par signature manuscrite sur support nomade (Doctoral dissertation, Tours).

Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information sciences, 507, 772-794.

Zeng, G. (2020). On the confusion matrix in credit scoring and its analytical properties. Communications in Statistics-Theory and Methods, 49(9), 2080-2093.

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Published

2022-10-04

How to Cite

Boluma Mangata, B., Ilunga Nakashama, D., Kadima Muamba, D., & Bukanga Christian, P. (2022). Implementation of an access control system based on bimodal biometrics with fusion of global decisions: Application to facial recognition and fingerprints. Journal of Computing Research and Innovation, 7(2), 43–53. https://doi.org/10.24191/jcrinn.v7i2.289

Issue

Section

General Computing

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