Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


dayton1353372694.pdf (1.89 MB)

ETD Abstract Container

Abstract Header

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

Alex, Ann Theja
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694

Abstract Details

2012, Master of Science (M.S.), University of Dayton, Electrical Engineering.
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.
Vijayan K. Asari (Committee Chair)
Tarek M. Taha (Committee Member)
Eric J. Balster (Committee Member)
87 p.
Computer Engineering; Computer Science; Electrical Engineering
Face recognition; Face sketch recognition; String Matching; Smith Waterman Algorithm; Edge features; Biometrics

Recommended Citations

Citations

  • Alex, A. T. (2012). Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694

    APA Style (7th edition)

  • Alex, Ann Theja. Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition. 2012. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694.

    MLA Style (8th edition)

  • Alex, Ann Theja. "Local Alignment of Gradient Features for Face Photo and Face Sketch Recognition." Master's thesis, University of Dayton, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1353372694

    Chicago Manual of Style (17th edition)

dayton1353372694
804
© 2012, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.