Phalguni Gupta
An Efficient Vein Pattern-based Recognition System.
Fri, 05/07/2010 - 15:01 — SomNambulAuthors: Mohit Soni, Phalguni Gupta, Sandesh Gupta, M.S. Rao
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper presents an efficient human recognition system based on vein
pattern from the palma dorsa. A new absorption based technique has been
proposed to collect good quality images with the help of a low cost camera and
light source. The system automatically detects the region of interest from the
image and does the necessary preprocessing to extract features. A Euclidean
Distance based matching technique has been used for making the decision.Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition.
Tue, 04/13/2010 - 15:02 — SomNambulAuthors: Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta, Massimo Tistarelli
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper presents a robust and dynamic face recognition technique based on
the extraction and matching of devised probabilistic graphs drawn on SIFT
features related to independent face areas. The face matching strategy is based
on matching individual salient facial graph characterized by SIFT features as
connected to facial landmarks such as the eyes and the mouth. In order to
reduce the face matching errors, the Dempster-Shafer decision theory is applied
to fuse the individual matching scores obtained from each pair of salient
facial features.Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-based Improved K-Medoids Partitioning.
Tue, 04/13/2010 - 15:02 — SomNambulAuthors: Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper presents a feature level fusion approach which uses the improved
K-medoids clustering algorithm and isomorphic graph for face and palmprint
biometrics. Partitioning around medoids (PAM) algorithm is used to partition
the set of n invariant feature points of the face and palmprint images into k
clusters. By partitioning the face and palmprint images with scale invariant
features SIFT points, a number of clusters is formed on both the images. Then
on each cluster, an isomorphic graph is drawn.Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory.
Wed, 03/31/2010 - 15:02 — SomNambulAuthors: Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper uses Support Vector Machines (SVM) to fuse multiple classifiers
for an offline signature system. From the signature images, global and local
features are extracted and the signatures are verified with the help of
Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers.
SVM is used to fuse matching scores of these matchers.Robust multi-camera view face recognition.
Wed, 03/31/2010 - 15:02 — SomNambulAuthors: Dakshina Ranjan Kisku, Hunny Mehrotra, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper presents multi-appearance fusion of Principal Component Analysis
(PCA) and generalization of Linear Discriminant Analysis (LDA) for multi-camera
view offline face recognition (verification) system. The generalization of LDA
has been extended to establish correlations between the face classes in the
transformed representation and this is called canonical covariate.Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm.
Wed, 02/03/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta, Massimo Tistarelli
Subjects: Computer Vision and Pattern Recognition
AbstractFaces are highly deformable objects which may easily change their appearance
over time. Not all face areas are subject to the same variability. Therefore
decoupling the information from independent areas of the face is of paramount
importance to improve the robustness of any face recognition technique. This
paper presents a robust face recognition technique based on the extraction and
matching of SIFT features related to independent face areas. Both a global and
local (as recognition from parts) matching strategy is proposed.Feature Level Clustering of Large Biometric Database.
Wed, 02/03/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Hunny Mehrotra, Phalguni Gupta, V. Bhawani Radhika, Banshidhar Majhi
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper proposes an efficient technique for partitioning large biometric
database during identification. In this technique feature vector which
comprises of global and local descriptors extracted from offline signature are
used by fuzzy clustering technique to partition the database. As biometric
features posses no natural order of sorting, thus it is difficult to index them
alphabetically or numerically. Hence, some supervised criteria is required to
partition the search space.SIFT-based Ear Recognition by Fusion of Detected Keypoints from Color Similarity Slice Regions.
Wed, 02/03/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Hunny Mehrotra, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractEar biometric is considered as one of the most reliable and invariant
biometrics characteristics in line with iris and fingerprint characteristics.
In many cases, ear biometrics can be compared with face biometrics regarding
many physiological and texture characteristics. In this paper, a robust and
efficient ear recognition system is presented, which uses Scale Invariant
Feature Transform (SIFT) as feature descriptor for structural representation of
ear images.Feature Level Fusion of Biometrics Cues: Human Identification with Doddingtons Caricature.
Wed, 02/03/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper presents a multimodal biometric system of fingerprint and ear
biometrics. Scale Invariant Feature Transform (SIFT) descriptor based feature
sets extracted from fingerprint and ear are fused. The fused set is encoded by
K-medoids partitioning approach with less number of feature points in the set.
K-medoids partition the whole dataset into clusters to minimize the error
between data points belonging to the clusters and its center. Reduced feature
set is used to match between two biometric sets.Fusion of Multiple Matchers using SVM for Offline Signature Identification.
Wed, 02/03/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper uses Support Vector Machines (SVM) to fuse multiple classifiers
for an offline signature system. From the signature images, global and local
features are extracted and the signatures are verified with the help of
Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers.
SVM is used to fuse matching scores of these matchers.SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis.
Tue, 01/26/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Hunny Mehrotra, Jamuna Kanta Sing, Phalguni Gupta
Subjects: Computer Vision and Pattern Recognition
AbstractIdentity verification of authentic persons by their multiview faces is a real
valued problem in machine vision. Multiview faces are having difficulties due
to non-linear representation in the feature space. This paper illustrates the
usability of the generalization of LDA in the form of canonical covariate for
face recognition to multiview faces. In the proposed work, the Gabor filter
bank is used to extract facial features that characterized by spatial
frequency, spatial locality and orientation.
User login
Loading