scholarly journals Human Ear Recognition Using Geometrical Features Extraction

2015 ◽  
Vol 65 ◽  
pp. 529-537 ◽  
Author(s):  
Asmaa Sabet Anwar ◽  
Kareem Kamal A. Ghany ◽  
Hesham Elmahdy
Keyword(s):  
Features Extraction ◽  
Ear Recognition ◽  
Geometrical Features ◽  
Human Ear

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

Human ear recognition based on SIFT feature

2009 ◽  
Vol 29 (6) ◽  
pp. 1690-1693 ◽  
Author(s):  
Song-ze LEI ◽  
Hong-ge YAO ◽  
Min QI ◽  
Chong-yang HAO
Keyword(s):  
Sift Feature ◽  
Ear Recognition ◽  
Human Ear

Edge Detection and Contour Based Ear Recognition Scheme

2019 ◽  
Vol 12 (3) ◽  
pp. 74-92
Author(s):  
Deven Trivedi ◽  
Rohit Thanki ◽  
Surekha Borra
Keyword(s):  
Pattern Recognition ◽  
Histogram Equalization ◽  
Biometric Data ◽  
Snake Model ◽  
Ear Recognition ◽  
Computer Vision Technology ◽  
Human Ear ◽  
Edge Features ◽  
Matching Score ◽  
Better Than

In recent days, with the advancements in computer vision technology pattern recognition for biometric data has been the focus of many researchers. The human ear can be used to assist in the recognition of an individual. In this article, a new scheme for ear recognition is presented, based on edge features such as the helix shape and contours between the edge pixels. First, an ear image is detected from the acquired image using a snake model-based image segmentation technique, and then histogram equalization is applied to form an enhanced ear image. After that, an Infinite Symmetric Exponential Filter (ISEF) edge is applied to the image, the contouring of edges is calculated, and then the contour values of pixels are extracted as ear features. Finally, the ear matching is performed between query ear features and enrolled ear features. Based on the matching score, the decision about individual authentication is performed. The experimental results showed that this proposed scheme performs better than existing schemes in the literature.

Human ear recognition based on local multi-scale LBP features with city-block distance

2018 ◽  
Vol 78 (11) ◽  
pp. 14425-14441 ◽  
Author(s):  
Zineb Youbi ◽  
Larbi Boubchir ◽  
Abdelhani Boukrouche
Keyword(s):  
City Block ◽  
Ear Recognition ◽  
Multi Scale ◽  
Human Ear ◽  
City Block Distance
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