Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition

Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition

Bibliographic Details
Main Author: Alex, Ann Theja
Language:English
Published: University of Dayton / OhioLINK 2012
Subjects:
Computer Engineering
Computer Science
Electrical Engineering
Face recognition
Face sketch recognition
String Matching
Smith Waterman Algorithm
Edge features
Biometrics
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-dayton13533726942021-08-03T05:35:56Z Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition Alex, Ann Theja Computer Engineering Computer Science Electrical Engineering Face recognition Face sketch recognition String Matching Smith Waterman Algorithm Edge features Biometrics Automatic recognition of human faces (face photo recognition) irrespective of the expression variations and occlusions is a challenging problem. In the proposed technique, the edges of a face are identified, and a feature string is created from edge pixels. This forms a symbolic descriptor corresponding to the edge image referred to as 'edge-string'. The 'edge-strings' are then compared using the Smith-Waterman algorithm to match them. The class corresponding to each image is identified based on the number of string primitives that match. This method needs only a single training image per class. The proposed technique is also applicable to face sketch recognition. In face sketch recognition, a sketch drawn based on the descriptions of the victims or witnesses is compared against the photos in the mug shot database to facilitate a faster investigation. The effectiveness of the proposed method is compared with state-of-the-art algorithms on several databases. The method is observed to give promising results for both face photo recognition and face sketch recognition. 2012 English text University of Dayton / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694 http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Computer Engineering
Computer Science
Electrical Engineering
Face recognition
Face sketch recognition
String Matching
Smith Waterman Algorithm
Edge features
Biometrics
spellingShingle Computer Engineering
Computer Science
Electrical Engineering
Face recognition
Face sketch recognition
String Matching
Smith Waterman Algorithm
Edge features
Biometrics
Alex, Ann Theja
Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition
author Alex, Ann Theja
author_facet Alex, Ann Theja
author_sort Alex, Ann Theja
title Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition
title_short Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition
title_full Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition
title_fullStr Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition
title_full_unstemmed Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition
title_sort local alignment of gradient features for face photo and face sketch recognition
publisher University of Dayton / OhioLINK
publishDate 2012
url http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694
work_keys_str_mv AT alexanntheja localalignmentofgradientfeaturesforfacephotoandfacesketchrecognition
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