Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have disadvantage in which it can be easily to be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for feature extraction method. The wavelet packet decomposition feature is represented in the average wavelet, root mean squared (RMS) wavelet and power wavelet were selected as features to extract a meaningful information from the original EEG signal based on the visual representation. These features were applied to classify between familiar and unfamiliar image responses (visual representation) and to recognize 13 subjects by using Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Random Forest (RF). The analysis of the classification between familiar and unfamiliar images responses obtained that gamma frequency (30 – 45 Hz) achieved the highest correct recognition rate (CRR) and KNN obtained the accuracy of 92.8% was obtained with KNN in the classification between familiar and unfamiliar image responses. Using the gamma frequency band, the classification between the EEG responses of the 13 subjects was evaluated using the percentage of false acceptance rate (FAR) and false rejection rate (FRR). From the overall view, the value of FAR is lower than FRR. These values were used in authentication system as threshold for security level. As the result of classification between the subjects, SVM performed better compared as KNN and RF in which the error rate for acceptance of unauthorized person and rejection of authorized person were the lowest.
Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis or TB Bacilli. Currently, the classification of TB bacilli is carried out by microbiologist by using Ziehl-Nielsen (ZN) stained smear sputum slide under a light microscopy. However, the manual evaluation is time-consuming and lead to slow decision. Furthermore, the sensitivity is less due to incline of human error which lead to inaccurate conclusion. Therefore, this study proposes an intelligence identification and counting system to detect the presence of TB bacilli in the ZN-stained smear sputum image. This system is designed to identify the presence of TB bacilli and count the number of TB bacilli by applying digital image processing and artificial intelligence techniques. In image acquisition, there are 70 samples images of ZN-stained smear sputum image were collected from Hospital Universiti Sains Malaysia (HUSM) Kubang Kerian, Kota Bharu, Kelantan, Malaysia. The image processing technique consists of contrast enhancement, segmentation, and feature extraction. The contrast of original image was enhanced by the combination of global enhancement, local enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, the enhanced image was segmented using color thresholding and the features were extracted consists of on 18 colour features, 15 shape features and 5 texture features. Afterward, the features underwent feature selection to select the relevant features by using Neighborhood Component Analysis (NCA) and ReliefF Analysis. The study showed that there are relevant features were chosen by ReliefF at feature weight more than 0.004 including (8 colour features, 11 shape feature and 3 texture features) for improving the performance and accuracy of Multilayer Perceptron (MLP) trained by Scaled Conjugate Gradient (SCG). For classification process, MLP, k-Nearest Neighborhood (k-NN) and Support Vector Machine (SVM) are used with 6 folds cross-validation. It was found that MLP has the highest of accuracy, sensitivity and specificity with 93.8%, 93.4% and 94.1% respectively.
Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have the disadvantage in which they can easily be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for the feature extraction method. The wavelet packet decomposition feature is represented, root mean squared (RMS) wavelet features to extract a piece of meaningful information from the original EEG signal. These features were applied to classify between 15 subjects by using Support Vector Machine (SVM). The channel reduction was conducted to investigate the brain lobe effectiveness during the paradigms of familiar and unfamiliar EEG signals which the channel reduction is based on the brain lobes (temporal, occipital, parietal, and frontal). As a result, the above 14 channels obtained the best performance of the system which is 97.44% of correct recognition rate (CRR). The analysis of the paradigms among familiar only, unfamiliar only, and both familiar and unfamiliar was conducted to evaluate the contribution of the paradigms. The results show that 14 channels obtained the best familiar paradigms while the other contributed by unfamiliar. The result is promising because the CRR computed above 90%, however further analysis of channel reduction has to be work to obtain specific channel to develop the small number of channel for comfort and convenience biometric sensor which is suitable for future authentication.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.