Massimo Tistarelli
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.Face Identification by SIFT-based Complete Graph Topology.
Wed, 02/03/2010 - 16:01 — SomNambulAuthors: Dakshina Ranjan Kisku, Ajita Rattani, Enrico Grosso, Massimo Tistarelli
Subjects: Computer Vision and Pattern Recognition
AbstractThis paper presents a new face identification system based on Graph Matching
Technique on SIFT features extracted from face images. Although SIFT features
have been successfully used for general object detection and recognition, only
recently they were applied to face recognition. This paper further investigates
the performance of identification techniques based on Graph matching topology
drawn on SIFT features which are invariant to rotation, scaling and
translation.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.
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