scholarly journals Human Identification Based on Geometric Feature Extraction Using a Number of Biometric Systems Available: Review

2016 ◽  
Vol 9 (2) ◽  
pp. 140 ◽  
Author(s):  
Eman Fares Al Mashagba
Keyword(s):  
Feature Extraction ◽  
Human Body ◽  
Human Identification ◽  
Geometric Feature ◽  
Geometric Features ◽  
Font Size ◽  
Biometric Technology ◽  
Geometric Feature Extraction ◽  
Biometric Systems ◽  
Eye Region

<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-ansi-language: EN-US; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Biometric technology has attracted much attention in biometric recognition. Significant online and offline applications satisfy security and human identification based on this technology. Biometric technology identifies a human based on unique features possessed by a person. Biometric features may be physiological or behavioral. A physiological feature is based on the direct measurement of a part of the human body such as a fingerprint, face, iris, blood vessel pattern at the back of the eye, vascular patterns, DNA, and hand or palm scan recognition. A behavioral feature is based on data derived from an action performed by the user. Thus, this feature measures the characteristics of the human body such as signature/handwriting, gait, voice, gesture, and keystroke dynamics. A biometric system is performed as follows: acquisition, comparison, feature extraction, and matching. The most important step is feature extraction, which determines the performance of human identification. Different methods are used for extraction, namely, appearance- and geometry-based methods. This paper reports on a review of human identification based on geometric feature extraction using several biometric systems available. We compared the different biometrics in biometric technology based on the geometric features extracted in different studies. Several biometric approaches have more geometric features, such as hand, gait, face, fingerprint, and signature features, compared with other biometric technology. Thus, geometry-based method with different biometrics can be applied simply and efficiently. The eye region extracted from the face is mainly used in face recognition. In addition, the extracted eye region has more details as the iris features.</span>

scholarly journals An airfoil geometric-feature extraction and discrepant data fusion learning method

2022 ◽  
Author(s):  
Yu Xiang ◽  
Liwei Hu ◽  
Jun Zhang ◽  
Wenyong Wang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Feature Fusion ◽  
Geometric Feature ◽  
Geometric Features ◽  
Prediction Errors ◽  
Geometrical Meaning ◽  
Feature Extraction Method ◽  
Geometric Feature Extraction ◽  
Latent Features

Abstract The perception of geometric-features of airfoils is the basis in aerodynamic area for performance prediction, parameterization, aircraft inverse design, etc. There are three approaches to percept the geometric shape of airfoils, namely manual design of airfoil geometry parameters, polynomial definition and deep learning. The first two methods directly define geometric-features or polynomials of airfoil curves, but the number of extracted features is limited. Deep learning algorithms can extract a large number of potential features (called latent features). However, the features extracted by deep learning lack explicit geometrical meaning. Motivated by the advantages of polynomial definition and deep learning, we propose a geometric-feature extraction method (named Bézier-based feature extraction, BFE) for airfoils, which consists of two parts: manifold metric feature extraction and geometric-feature fusion encoder (GF encoder). Manifold metric feature extraction, with the help of the Bézier curve, captures manifold metrics (a sort of geometric-features) from tangent space of airfoil curves, and the GF-encoder combines airfoil coordinate data and manifold metrics together to form novel fused geometric-features. To validate the feasibility of the fused geometric-features, two experiments based on the public UIUC airfoil dataset are conducted. Experiment I is used to extract manifold metrics of airfoils and export the fused geometric-features. Experiment II, based on the Multi-task learning (MTL), is used to fuse the discrepant data (i.e., the fused geometric-features and the flight conditions) to predict the aerodynamic performance of airfoils. The results show that the BFE can generate more smooth and realistic airfoils than Auto-Encoder, and the fused geometric-features extracted by BFE can be used to reduce the prediction errors of C L and C D .

Geometric Feature Extraction of Ship in High-resolution Synthetic Aperture Radar Images

2018 ◽  
Vol 47 (1) ◽  
pp. 110001
Author(s):  
熊伟 XIONG Wei ◽  
徐永力 XU Yong-li ◽  
崔亚奇 CUI Ya-qi ◽  
李岳峰 LI Yue-feng
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Geometric Feature ◽  
Radar Images ◽  
Geometric Feature Extraction ◽  
Aperture Radar

Geometric feature extraction by FTAs for finger‐based biometrics system

2016 ◽  
Vol 6 (3) ◽  
pp. 157-164 ◽  
Author(s):  
Mohd Shahrimie Mohd Asaari ◽  
Shahrel Azmin Suandi ◽  
Bakhtiar Affendi Rosdi
Keyword(s):  
Feature Extraction ◽  
Geometric Feature ◽  
Geometric Feature Extraction

Research of Face Identification Method Based on Geometric Feature Extraction and the Enlargement of Image Interpolation with Scientific Image Materials

2011 ◽  
Vol 63-64 ◽  
pp. 846-849
Author(s):  
Jian Ni ◽  
Yu Duo Li
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Interpolation Method ◽  
Image Interpolation ◽  
Geometric Feature ◽  
Face Identification ◽  
Human Face ◽  
Training Experience ◽  
Geometric Feature Extraction ◽  
Vector Weight

To achieve human face identification, this paper adopts the method of geometric feature extraction and the enlargement of image interpolation on the basis of the completion of face detection. First of all, the input digital image will be normalized to reduce the complexity of the image, and then the feature of human face will be extract. With the feature information extracted, we can construct the feature vector and assign different weights to different feature vector. Weight is interpreted as the EXP obtained after a large amount of training experience is gained. Finally, to get the similarity of picture, the bilinear interpolation method is adopted on the basis of the nearest interpolation. Thus, we will get the results of face identification according to the similarity quality. Through the development and implementation of practical programming, this paper proves the feasibility of such method.

scholarly journals Geometric Feature Extraction by a Multimarked Point Process

2010 ◽  
Vol 32 (9) ◽  
pp. 1597-1609 ◽  
Author(s):  
Florent Lafarge ◽  
Georgy Gimel'farb ◽  
Xavier Descombes
Keyword(s):  
Feature Extraction ◽  
Point Process ◽  
Geometric Feature ◽  
Geometric Feature Extraction
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