Human Ear Detection From 3D Side Face Range Images

2007 ◽  
pp. 133-155 ◽  
Author(s):  
H. Chen ◽  
B. Bhanu
Keyword(s):  
Range Images ◽  
Ear Detection ◽  
Side Face ◽  
Human Ear

Human ear detection of transient fetal heart accelerations

1980 ◽  
Vol 1 (1) ◽  
pp. 24-26
Author(s):  
I. J. Robbé ◽  
P. Curzen
Keyword(s):  
Fetal Heart ◽  
Ear Detection ◽  
Human Ear

HEARD: An automatic human EAR detection technique

2012 ◽  
Author(s):  
Nermin Kamal Abdel Wahab ◽  
Elsayed Essa Hemayed ◽  
Magda Bahaa Fayek
Keyword(s):  
Detection Technique ◽  
Ear Detection ◽  
Human Ear

scholarly journals Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huy Nguyen Quoc ◽  
Vinh Truong Hoang
Keyword(s):  
Real Time ◽  
Detection System ◽  
Real Life ◽  
Identification System ◽  
Detection Algorithms ◽  
Ear Detection ◽  
Unique Shape ◽  
Human Ear ◽  
Real Time Application ◽  
Face Detector

Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.

Human ear detection in the thermal infrared spectrum

2012 ◽  
Author(s):  
Ayman Abaza ◽  
Thirimachos Bourlai
Keyword(s):  
Infrared Spectrum ◽  
Thermal Infrared ◽  
Ear Detection ◽  
Human Ear

scholarly journals Human Ear Detection for Biometries

2005 ◽  
Vol 15 (7) ◽  
pp. 813-816
Author(s):  
Young-Baek Kim ◽  
Sang-Yong Rhee
Keyword(s):  
Ear Detection ◽  
Human Ear

scholarly journals AN EMPIRICAL STUDY AND EVALUATION ON AUTOMATIC EAR DETECTION

2020 ◽  
pp. 575-582
Author(s):  
K. R. Resmi ◽  
G. Raju
Keyword(s):  
Empirical Study ◽  
Complex Structure ◽  
Face Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Ear Detection ◽  
Complex Process ◽  
Human Ear ◽  
The Given ◽  
Biometric Trait

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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