Face Recognition Using Facial Landmarks.
Date of Submission
December 2008
Date of Award
Winter 12-12-2009
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Electronics and Communication Sciences Unit (ECSU-Kolkata)
Supervisor
Chanda, Bhabatosh (ECSU-Kolkata; ISI)
Abstract (Summary of the Work)
In today's networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. Recently, technology became available to allow verification of "true" individual identity. This technology is based on a field called "biometrics". Face recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. In this report we have presented a novel approach for Face recognition that works based on facial landmarks/features in a face such as eyes, nose and lips etc. In this approach we, first locate the probable positions of these facial features using gradient technique. Template Matching is used over a predefined area around the probable positions to detect the exact location of these landmarks. We extract statistical and geometrical features for representing the facial landmarks and their relative location. We have used two different similarity/distance measures in recognition process. The algorithm is implemented using VC++ and tested on various databases
Control Number
ISI-DISS-2008-221
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
http://dspace.isical.ac.in:8080/jspui/handle/10263/6381
Recommended Citation
Nagamalla, Srinivas, "Face Recognition Using Facial Landmarks." (2009). Master’s Dissertations. 70.
https://digitalcommons.isical.ac.in/masters-dissertations/70
Comments
ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843083