Multi-directional two-dimensional PCA with matching score level fusion for face recognition

2012 ◽  
Vol 23 (1) ◽  
pp. 169-174 ◽  
Author(s):  
Qi Zhu ◽  
Yong Xu
Keyword(s):  
Face Recognition ◽  
Two Dimensional ◽  
Score Level Fusion ◽  
Matching Score ◽  
Level Fusion

Multimodal score level fusion for recognition using face and palmprint

2020 ◽  
pp. 002072092092966
Author(s):  
Milind E Rane ◽  
Umesh S Bhadade
Keyword(s):  
Recognition System ◽  
Acceptance Rate ◽  
Face Database ◽  
Biometric Recognition ◽  
False Acceptance Rate ◽  
Score Level Fusion ◽  
Score Fusion ◽  
Matching Score ◽  
Level Fusion ◽  
Fusion Approach

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

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.

A NEW APPROACH FOR FACE-IRIS MULTIMODAL BIOMETRIC RECOGNITION USING SCORE FUSION

2013 ◽  
Vol 27 (03) ◽  
pp. 1356004 ◽  
Author(s):  
MARYAM ESKANDARI ◽  
ÖNSEN TOYGAR ◽  
HASAN DEMIREL
Keyword(s):  
Recognition Performance ◽  
Recognition System ◽  
Extraction Methods ◽  
Score Level Fusion ◽  
New Approach ◽  
Fusion Methods ◽  
Score Fusion ◽  
Multimodal Biometric Recognition ◽  
Matching Score ◽  
Level Fusion

In this paper, a new approach based on score level fusion is presented to obtain a robust recognition system by concatenating face and iris scores of several standard classifiers. The proposed method concatenates face and iris match scores instead of concatenating features as in feature-level fusion. The features from face and iris are extracted using local and global feature extraction methods such as PCA, subspace LDA, spPCA, mPCA and LBP. Transformation-based score fusion and classifier-based score fusion are then involved in the process to obtain, concatenate and classify the matching scores. Different fusion techniques at matching score level, feature level and decision level are compared with the proposed method to emphasize improvement and effectiveness of the proposed method. In order to validate the proposed scheme, a combined database is formed using ORL and BANCA face databases together with CASIA and UBIRIS iris databases. The results based on recognition performance and ROC analysis demonstrate that the proposed score level fusion achieves a significant improvement over unimodal methods and other multimodal face-iris fusion methods.

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.

Synthesis Score Level Fusion Based Multifarious Classifier for Multi-Biometrics Applications

2019 ◽  
Vol 9 (8) ◽  
pp. 1673-1680
Author(s):  
J. Vaijayanthimala ◽  
T. Padma
Keyword(s):  
Feature Extraction ◽  
Performance Metrics ◽  
Feature Matching ◽  
Score Level Fusion ◽  
Data Glove ◽  
Verification Method ◽  
Summation Formulae ◽  
The Face ◽  
Matching Score ◽  
Level Fusion

In this paper, we are presenting a face and signature recognition method from a large dataset with the different pose and multiple features. Initially, Face and corresponding signature are detected from devices for further pre-processing. Face recognition is the first stage of a system then the signature verification will be done. The proposed Legion feature based verification method will be developed using four important steps like, (i) feature extraction from face and data glove signals using feature Extraction. The various Features like Local binary pattern, shape and geometrical features of face, then the global and local features of the signatures were extracted. (ii) Score match normalization is used to enhance the recognition accuracy using min–max and median estimations. (iii) Then the match scores are evaluated using synthesis score level fusion based feature matching through Euclidean distance, (iv) Recognition based on the final score. Finally based on the feature library the face image and signature can be recognized. The similarity measurement is done by using Synthesis score level fusion (SSF) based multifarious Neural network (MNN) Classifier with weighted summation formulae where two weights will be optimally found out using Adapted motion search optimization algorithm. Finally SSF-MNN based matching score fusion based decision classifier to determine recognized and non-recognized biometrics. Moreover, in comparative analysis, a proposed technique is compared with the existing method by several performance metrics and the proposed SSF-MNN technique efficiently recognize the face images and corresponding signature from the input databases than the existing technique.

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
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