Print ISSN: 1811-9212

Online ISSN: 2617-3352

Keywords : Impostor


Impostor Recognition Based Voice Authentication by Applying Three Machine Learning Algorithms

Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2021, Volume 21, Issue 3, Pages 112-124
DOI: https://doi.org/10.33103/uot.ijccce.21.3.10

As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform for speaker Authentication to recognize impostors. To boost the audios usable in real environments, it was suggested the preprocessing of audio, like noise decreasing and voiced improving. Mel Frequency Cepstral Coefficients (MFCC) and the four features (Amplitude, Zero Crossing, Mean, and Standard Division) are extracted for all audio metrics, straight beside their differentials and accelerations. Then, Vector Quantization (VQ) is applied to these files. The algorithms were prepared and examined on two datasets, by applying k-fold cross-validation. The preparation for testing and comparing the three (ML) approaches are as follows: Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR). The result of the (SVM) algorithm average accuracy of 96.33 percent was superior.

Impostor Detection Based Finger Veins Applying Machine Learning Methods

Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil

IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2021, Volume 21, Issue 3, Pages 98-111
DOI: https://doi.org/10.33103/uot.ijccce.21.3.9

Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measures were carried out on two different datasets, which are the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) Dataset and the University of Twente Finger Veins (UTFV) dataset. The classifier with the best results was Support Vector Machine (SVM) and Linear Regression (LR) had the lowest classifier accuracy.