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

Data Mining

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

User Verification System on Telebanking Using Kernel Based PCA

In this thesis, I suggest a way of using voice to make up for the weak points in current security systems and downsize the load of users by affording natural and convenient speaker verification. This thesis contains the basic technology to build a practical speaker verification system using neural networks and research for implementing the system, which guarantees reliability. Firstly, this thesis will focus on making up for the weak points in current existing methods by applying the characteristics of neural networks like learning ability, parallel processing ability and adaptability. And this thesis will advance to characteristic presentation and extracting method, technique of dividing voice signal, research on new neural network model to solve speaker verification problem, adaptability for changing environment such as DTW using kernel-based PCA, spectral subtraction. To make improvements in system performance and adaptability, we study a recognition model using a module structured neural network and evaluate its appropriateness and usefulness experimentally by implementing a prototype