8 research outputs found

    Biometric Systems

    Get PDF
    Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

    State of the Art in Face Recognition

    Get PDF
    Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

    Feature extraction and information fusion in face and palmprint multimodal biometrics

    Get PDF
    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
    corecore