This paper proposes a palmprint identification system using Finite Ridgelet Transform (FRIT) and Bayesian classifier.
FRIT is applied on the ROI (region of interest), which is extracted from palmprint image, to extract a set of distinctive
features from palmprint image. These features are used to classify with the help of Bayesian classifier. The proposed
system has been tested on CASIA and IIT Kanpur palmprint databases. The experimental results reveal better
performance compared to all well known systems.
This paper presents a palmprint based verification system using SIFT features and Lagrangian network graph technique.
We employ SIFT for feature extraction from palmprint images whereas the region of interest (ROI) which has been
extracted from wide palm texture at the preprocessing stage, is considered for invariant points extraction. Finally,
identity is established by finding permutation matrix for a pair of reference and probe palm graphs drawn on extracted
SIFT features. Permutation matrix is used to minimize the distance between two graphs. The propsed system has been
tested on CASIA and IITK palmprint databases and experimental results reveal the effectiveness and robustness of the
In this paper, fusion of Principal Component Analysis (PCA) and generalization of Linear Discriminant Analysis (LDA)
in the context of multiview face recognition is proposed. The generalization of LDA is extended to establish correlation
between face classes in the transformed representation, which is called canonical covariate. The proposed work uses
Gabor filter bank for extracting facial features characterized by spatial frequency, spatial locality and orientation to
compensate the variations in face that occur due to change in illumination, pose and facial expression. Convolution of
Gabor filter bank with face images produces Gabor face representations with high dimensional feature vectors. PCA and
canonical covariate are then applied on the Gabor face representations to reduce the high dimensional feature spaces into
low dimensional Gabor eigenfaces and Gabor canonical faces. Reduced eigenface vector and canonical face vector are
fused together using weighted mean fusion rule. Finally, support vector machines have been trained with augmented
fused set of features to perform recognition task. The proposed system has been evaluated with UMIST face database
and performs with higher recognition accuracy for multi-view face images.
This paper proposes the multimodal biometrics system for identity verification using four traits i.e., face, fingerprint, iris and signature. The proposed system is designed for applications where the training database contains a face, iris, two fingerprint images and/or one or two signature image(s) for each individual. The final decision is made by fusion at "matching score level architecture" in which feature vectors are created independently for query images and are then compared to the enrollment templates which are stored during database preparation for each biometric trait. Based on the proximity of feature vector and template, each subsystem computes its own matching score. These individual scores are finally combined into a total score, which is passed to the decision module. Multimodal system is developed through fusion of face, fingerprint, iris and signature recognition. This system is tested on IITK database and the overall accuracy of the system is found to be more than 97% accurate with FAR and FRR of 2.46% and 1.23% respectively.