Title page for ETD etd-05232007-093839


Type of Document Master's Thesis
Author Saleh, Mohamed Ibrahim
URN etd-05232007-093839
Title Using Ears for Human Identification
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Abbott, A. Lynn Committee Chair
Schaumont, Patrick Robert Committee Member
Wyatt, Christopher L. Committee Member
Keywords
  • Ear Recognition
  • Ear
  • Multimodal Biometrics
  • Biometric
  • Segmentation
  • Eigen-Face
  • Eigen-Ears
  • Pattern Recognition
  • Principal Components Analysis (PCA)
  • Face Recognition
Date of Defense 2007-05-07
Availability unrestricted
Abstract
Biometrics includes the study of automatic methods for distinguishing human beings based on physical or behavioral traits. The problem of finding good biometric features and recognition methods has been researched extensively in recent years. Our research considers the use of ears as a biometric for human recognition. Researchers have not considered this biometric as much as others, which include fingerprints, irises, and faces. This thesis presents a novel approach to recognize individuals based on their outer ear images through spatial segmentation. This approach to recognizing is also good for dealing with occlusions. The study will present several feature extraction techniques based on spatial segmentation of the ear image. The study will also present a method for classifier fusion. Principal components analysis (PCA) is used in this research for feature extraction and dimensionality reduction. For classification, nearest neighbor classifiers are used. The research also investigates the use of ear images as a supplement to face images in a multimodal biometric system. Our base eigen-ear experiment results in an 84% rank one recognition rate, and the segmentation method yielded improvements up to 94%. Face recognition by itself, using the same approach, gave a 63% rank one recognition rate, but when complimented with ear images in a multimodal system improved to 94% rank one recognition rate.
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