Mohd Zaizu Ilyas

Classification of EEG signals for brain-computer interface applications: Performance comparison

This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage is very important to get the robust BCI system. Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Logistic Regression (LR) were evaluated in this paper. A BCI competition IV — Dataset 1 is used for testing the classifiers. It is shown that LR and SVM are the most efficient classifier with the highest accuracy of 73.03% and 68.97%.

Investigation of information fusion in face and palmprint multimodal biometrics

This paper reviews several information fusion techniques and strategies in the application of multimodal biometrics system using face and palmprint images. Multimodal biometric is able to overcome several limitations in single modal biometric such as intra-class variations, less discriminative power, noise data and redundant features. By consolidating two kinds of modality a better performance can be achieved. Information fusion in multimodal biometrics can be carried out at three possible levels, i.e. feature, matching score and decision levels. Fusions at these three levels have their own attributes, thus this paper is aimed to compare their effectiveness. A specific fusion rule is necessary to combine the information at each level. Several numbers of analyses on verification and identification shows matching score fusion is able to achieve the best performance which is 98% recognition rates and 98.5% GAR at 0.1% FAR when tested using AR face and PolyU palmprint datasets.

Improving speaker verification using MFCC order

This paper presents a text-dependent speaker verification using Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machine (SVM). Mel-Frequency Cepstral Coefficients technique has been used to extract the characteristic from the recorded voice spoken by the user and SVM is used to classify the all models of the speakers and impostors. A Malay spoken digit database is utilized for the training and testing. The aim of this paper is to improve the performance of SVM by selecting the best order of Mel-Frequency Cepstral Coefficients. Five types of Mel-Frequency Cepstral Coefficients order (5, 10, 15, 20, 25) have been tested and classified using SVM. It is shown that 20th and 25th order of MFCC achieved the best total success rate (TSR) and Equal Error Rate (EER).

Improving hybrid speaker verification in noisy environments using least mean-square adaptive filters

In this paper, we present a hybrid speaker verification system based on the Hidden Markov Models (HMMs) and Vector Quantization(VQ) and Least Mean-Square (LMS) adaptive filtering. The aim of using hybrid speaker verification is to improve the HMMs performance, while LMS adaptive filtering is to improve the hybrid speaker verification performance in noisy environments. A Malay spoken digit database is used for the training and testing. It is shown that, in a clean environment a Total Success Rate (TSR) of 99.97% is achieved using hybrid VQ and HMMs. For speaker verification, the true speaker rejection rate is 0.06% while the impostor acceptance rate is 0.03% and the equal error rate (EER) is 11.72%. In noisy environments without LMS adaptive filtering TSRs of between 62.57%-76.80% are achieved for Signal to Noise Ratio (SNR) of 0-30 dBs. Meanwhile, after LMS filtering, TSRs of between 77.31%-76.87% are achieved for SNRs of 0-30 dB.