Abstract
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.
Highlights
Authentication of an individual is important in various security applications
The difficulties of traditional methods are removed by using biometric traits which use universal, permanent and unique characteristics of a person
GTAV is a face database and only side face images which contain ear portions are used for ear detection works
Summary
Traditional security methods are based on tokens (like ID card) and knowledge (Passwords, PIN etc.). Biometric authentication is mainly based on face, finger print, iris, retina, ear, vain and speech. Ear recognition is not affected by changes in pose and facial expressions [2]. The recognition performance of biometrics like face changes with pose and facial expressions. Ear can be used in applications like security, access control, surveillance, etc. Various methods are used for ear detection. Automatic ear localization or detection is still a difficult task. Recent studies in automatic ear detection and recognition using CNN have shown very good results with the use of lots of training data and high GPU power. Further a method is suggested and experimented for automatic evaluation of ear detection methods
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