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Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem
of identifying a speaker from its voice regardless of the content (i.e.
text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system.
A novel approach towards speaker identification is developed using
wavelet analysis, and multiple neural networks including Probabilistic
Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND
voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state-
of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA).
Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear.
Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that
the proposed scheme is one of the best candidates for the fusion of
face and voice due to its low computational time and high recognition accuracy
An Overview of Multi-layer Security System Using Biometrics
After years of research on biometrics, still the authentication and verification of a human being is at risk. Many challenges have been faced by the system to authenticate a human on the basis of their biometric trait. The design of this paper is such that it focuses on three main biometric techniques to extract the various features of a human body contour for authentication and verification purposes. This paper presents an approach towards voice recognition patterns, face geometry patterns and gait pattern analysis to increase the accuracy and precision of the system
Landsat D Thematic Mapper image dimensionality reduction and geometric correction accuracy
To characterize and quantify the performance of the Landsat thematic mapper (TM), techniques for dimensionality reduction by linear transformation have been studied and evaluated and the accuracy of the correction of geometric errors in TM images analyzed. Theoretical evaluations and comparisons for existing methods for the design of linear transformation for dimensionality reduction are presented. These methods include the discrete Karhunen Loeve (KL) expansion, Multiple Discriminant Analysis (MDA), Thematic Mapper (TM)-Tasseled Cap Linear Transformation and Singular Value Decomposition (SVD). A unified approach to these design problems is presented in which each method involves optimizing an objective function with respect to the linear transformation matrix. From these studies, four modified methods are proposed. They are referred to as the Space Variant Linear Transformation, the KL Transform-MDA hybrid method, and the First and Second Version of the Weighted MDA method. The modifications involve the assignment of weights to classes to achieve improvements in the class conditional probability of error for classes with high weights. Experimental evaluations of the existing and proposed methods have been performed using the six reflective bands of the TM data. It is shown that in terms of probability of classification error and the percentage of the cumulative eigenvalues, the six reflective bands of the TM data require only a three dimensional feature space. It is shown experimentally as well that for the proposed methods, the classes with high weights have improvements in class conditional probability of error estimates as expected
Feature extraction and information fusion in face and palmprint multimodal biometrics
ThesisMultimodal biometric systems that integrate the biometric traits from several
modalities are able to overcome the limitations of single modal biometrics. Fusing
the information at an earlier level by consolidating the features given by different
traits can give a better result due to the richness of information at this stage. In this
thesis, three novel methods are derived and implemented on face and palmprint
modalities, taking advantage of the multimodal biometric fusion at feature level.
The benefits of the proposed method are the enhanced capabilities in discriminating
information in the fused features and capturing all of the information required to
improve the classification performance. Multimodal biometric proposed here
consists of several stages such as feature extraction, fusion, recognition and
classification.
Feature extraction gathers all important information from the raw images. A
new local feature extraction method has been designed to extract information from
the face and palmprint images in the form of sub block windows. Multiresolution
analysis using Gabor transform and DCT is computed for each sub block window to
produce compact local features for the face and palmprint images. Multiresolution
Gabor analysis captures important information in the texture of the images while
DCT represents the information in different frequency components. Important
features with high discrimination power are then preserved by selecting several low
frequency coefficients in order to estimate the model parameters.
The local features extracted are fused in a new matrix interleaved method. The
new fused feature vector is higher in dimensionality compared to the original feature
vectors from both modalities, thus it carries high discriminating power and contains
rich statistical information. The fused feature vector also has larger data points in
the feature space which is advantageous for the training process using statistical
methods. The underlying statistical information in the fused feature vectors is
captured using GMM where several numbers of modal parameters are estimated
from the distribution of fused feature vector.
Maximum likelihood score is used to measure a degree of certainty to perform
recognition while maximum likelihood score normalization is used for classification
process. The use of likelihood score normalization is found to be able to suppress an
imposter likelihood score when the background model parameters are estimated
from a pool of users which include statistical information of an imposter. The
present method achieved the highest recognition accuracy 97% and 99.7% when
tested using FERET-PolyU dataset and ORL-PolyU dataset respectively.Universiti Malaysia Perlis and Ministry of Higher Education
Malaysi
Human face detection techniques: A comprehensive review and future research directions
Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection
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