Score level fusion method based on multiple oblique gradient operators for face recognition

2014 ◽  
Vol 75 (2) ◽  
pp. 819-837 ◽  
Author(s):  
Zhaokui Li ◽  
Lixin Ding ◽  
Yan Wang
Keyword(s):  
Face Recognition ◽  
Fusion Method ◽  
Score Level Fusion ◽  
Level Fusion

A Score-Level Fusion Method for Protecting Fingerprint and Palmprint Templates

2021 ◽  
pp. 1-11
Author(s):  
Mulagala Sandhya ◽  
Y. Sreenivasa Rao ◽  
Sahoo Biswajeet ◽  
Vallabhadas Dilip Kumar ◽  
Maurya Anup Kumar
Keyword(s):  
Fusion Method ◽  
Score Level Fusion ◽  
Level Fusion

Matching Score Level Fusion

2011 ◽  
pp. 305-327 ◽  
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Fusion Method ◽  
Score Level Fusion ◽  
Fusion Rules ◽  
Score Fusion ◽  
Basic Characteristics ◽  
Matching Score ◽  
Level Fusion

With this chapter we aims at describing several basic aspects of matching score level fusion. Section 14.1 provides a description of basic characteristics of matching score fusion in the form of introduction. Section 14.2 shows a number of matching score fusion rules. Section 14.3 surveys several typical normalization procedures of raw matching scores. Section 14.4 gives an example of matching score level fusion method. Finally, Section 14.5 provides several brief comments on matching score fusion.

Using the Original and Symmetrical Face Test Samples to Perform Two-Step Collaborative Representation for Face Recognition

2018 ◽  
Vol 33 (02) ◽  
pp. 1956001 ◽  
Author(s):  
Zhonghua Liu ◽  
Lin Zhang ◽  
Jiexin Pu ◽  
Gang Liu ◽  
Sen Liu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Classification Scheme ◽  
Test Sample ◽  
Collaborative Representation ◽  
Score Level Fusion ◽  
Face Classification ◽  
The Face ◽  
Number Of Classes ◽  
Level Fusion

Face recognition using sparse representation-based classification (SRC) is a new hot technique in recent years. However, the research indicates that it is the collaborative representation but not the [Formula: see text]-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a simple yet much more efficient face classification scheme, namely two-step collaborative representation-based classification (TSCRC) method. First, we exploit the symmetry of the face to generate new images of each test sample. Then, the original and new generated test samples are, respectively, used to perform TSCRC, which ultimately uses a small number of classes that are near to the test sample to represent and classify it. Finally, the score level fusion is taken to perform classification recognition. The experimental results clearly show that the proposed method has very competitive classification results.

Expression invariant face recognition using semidecimated DWT, Patch-LDSMT, feature and score level fusion

2015 ◽  
Vol 44 (4) ◽  
pp. 913-930 ◽  
Author(s):  
Hemprasad Patil ◽  
Ashwin Kothari ◽  
Kishor Bhurchandi
Keyword(s):  
Face Recognition ◽  
Score Level Fusion ◽  
Level Fusion

scholarly journals Score level fusion in representation-based classification method for face recognition

2020 ◽  
Vol 14 ◽  
pp. 174830262093094
Author(s):  
Zi-Qi Li ◽  
Jun Sun ◽  
Xiao-Jun Wu ◽  
He-Feng Yin
Keyword(s):  
Face Recognition ◽  
Least Square ◽  
Classification Method ◽  
Collaborative Representation ◽  
Score Level Fusion ◽  
Training Samples ◽  
Global Representation ◽  
Face Datasets ◽  
Representation Method ◽  
Level Fusion

Recent years have witnessed the success of representation-based classification method (RBCM) in the domain of face recognition. Collaborative representation-based classification (CRC) and linear regression-based classification (LRC) are two representative approaches. CRC is a global representation method which uses all training samples to represent test samples and utilizes representation residuals to perform classification, whereas LRC is a local representation method which exploits training samples from each class to represent test samples. Related researches indicate that the combination of LRC and CRC can fully exploit the representation residuals produced by them, thus improving the performance of RBCM. However, the representation coefficients obtained by CRC usually contain negative values which may result in overfitting problem. Therefore, to solve this problem to some extent, the combination of LRC and non-negative least square-based classification (NNLSC) is proposed in this paper. Experimental results on benchmark face datasets show that the proposed method is superior to the combination of LRC and CRC and other state-of-the-art RBCMs. The source code of our proposed method is available at https://github.com/li-zi-qi/score-level-fusion-of-NNLS-and-LRC .

scholarly journals A score level fusion method for eye movement biometrics

2016 ◽  
Vol 82 ◽  
pp. 207-215 ◽  
Author(s):  
Anjith George ◽  
Aurobinda Routray
Keyword(s):  
Eye Movement ◽  
Fusion Method ◽  
Score Level Fusion ◽  
Level Fusion
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