8 research outputs found
Biometric Systems
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
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
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