scholarly journals An affine view and illumination invariant iterative image matching approach for face recognition

2018 ◽  
Vol 7 (2.8) ◽  
pp. 42
Author(s):  
D Rajasekhar ◽  
T Jayachandra Prasad ◽  
K Soundararajan
Keyword(s):  
Face Recognition ◽  
Image Matching ◽  
Feature Detection ◽  
Scale Invariant ◽  
Data Set ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Affine Transforms

Feature detection and image matching constitutes two primary tasks in photogrammetric and have multiple applications in a number of fields. One such application is face recognition. The critical nature of this application demands that image matching algorithm used in recognition of features in facial recognition to be robust and fast. The proposed method uses affine transforms to recognize the descriptors and classified by means of Bayes theorem. This paper demonstrates the suitability of the proposed image matching algorithm for use in face recognition appli-cations. Yale facial data set is used in the validation and the results are compared with SIFT (Scale Invariant Feature Transform) based face recognition approach.

scholarly journals Portrayal Matching Algorithm By using Sift

2020 ◽  
Vol 9 (1) ◽  
pp. 2711-2713
Keyword(s):  
Image Matching ◽  
True Positive ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Image Identification ◽  
Invariant Feature ◽  
Feature Transform ◽  
Image Pairs ◽  
Matching Criteria ◽  
Scale Invariant Feature

Image identification and matching is one of the very difficult assignment in different areas of mainframe vie w. Scale-Invariant Feature Transform is an algorithm to perceive and represent specific features in portryals to further use them as an image matching criteria. In this paper, the extracted SIFT matching features are against various image distortions such as rotation, scaling, fisheye and motion distortion are evaluated and false and true positive rates for a large number of image pairs are calculated and presented.

An Improved SIFT Matching Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1232-1237 ◽  
Author(s):  
Can Ding ◽  
Chang Wen Qu ◽  
Feng Su
Keyword(s):  
High Dimension ◽  
Gradient Method ◽  
Feature Matching ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
New Feature ◽  
Scale Invariant Feature

The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve matching speed and keep matching precision at the same time.

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