Odour based human identification and classification using neural networks

Ahmed Qusay Sabri and Rayner Alfred (2019) Odour based human identification and classification using neural networks. International Journal of Recent Technology and Engineering (IJRTE), 8. ISSN 2277-3878

[img] Text
Odour based human identification and classification using neural networks-Abstract.pdf

Download (63kB)
[img] Text
Odour based human identification and classification using neural networks.pdf
Restricted to Registered users only

Download (540kB) | Request a copy


Biometrics permits an individual to be authenticated and identified by computer systems following on a set of verifiable and identifiable data that are precise and unique in nature. This mechanism constitutes a cutting-edge method of identifying an individual since it precisely establishes more explicit and direct connection with humans than mere passwords since biometrics tend to use measurable behavioral and physiological characteristics of human. In this paper, a framework for human identification is proposed distinctively based on specific human odour features. 15 samples of female and male human odour are collected from different age groups, only 15 effective Volatile Organic Compounds (VOCs) are chosen. In this paper, several diverse functions of neural network activation are tested such as Levenberg-Marquardt back propagation, Gradient descent back propagation, and Resilient back propagation. Besides, numerous neural network topologies are tested by means of variety hidden layers and different number of neurons and. Different energy functions were tested TAN- Sigmoid transfer, Linear transfer, and LOG- Sigmoid transfer. Considering the obtained results, employing two hidden layers with more neurons in the hidden layers- to be specific: 15 neurons in every layer- has yielded better accuracy in performance with an accuracy rate of 100%. The unsurpassed framework for algorithm learning to be used for human identification can be back propagation learning algorithm named the Levenberg-Marquardt. The best function for activation established in this paper is the function of TANSigmoid transfer. The performance accuracy consistency in recognizing human can be enhanced using a big number of study samples.

Item Type: Article
Keyword: VOC , Odour , Resilient backpropagation , Gradient descent backpropagation , Levenberg-marquardt backpropagation
Subjects: R Medicine > RA Public aspects of medicine
T Technology > T Technology (General)
Department: FACULTY > Faculty of Computing and Informatics
Depositing User: DG MASNIAH AHMAD -
Date Deposited: 22 Jul 2021 11:43
Last Modified: 22 Jul 2021 11:43
URI: https://eprints.ums.edu.my/id/eprint/30023

Actions (login required)

View Item View Item