AN EMPIRICAL STUDY AND EVALUATION ON AUTOMATIC EAR DETECTION
Keywords:Biometric, Ear detection, Morphological Operators, Template Matching, Banana Wavelets, Hough Transform
Biometric is one of the growing fields used in security, forensic and surveillance applications. Various types of physiological and behavioral biometrics are available today. Human ear is a passive physiological biometric. Ear is an important biometric trait due to many advantages over other biometric modalities. Because of its complex structure, face image detection is very challenging. Detection deals with finding or localizing the position of ear in the given profile face image. Various methods like manual, semiautomatic and automatic techniques are used for ear detection. Automatic ear localization is a complex process compared to manual ear cropping. This paper presents an empirical study and evaluation of four different existing ear detection techniques with our proposed method based on banana wavelets and circular Hough transform. A comparative analysis of the five algorithms in terms of detection accuracy is presented. The detection accuracy was calculated by means of manual as well as automatic verification.
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