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.

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.

scholarly journals Incorporating Metadata in Multibiometric Score-Level Fusion: an Optimized Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 5290-5305
Keyword(s):  
Skin Color ◽  
Affective Computing ◽  
Biometric Data ◽  
Score Level Fusion ◽  
New Concepts ◽  
Biometric Systems ◽  
The Face ◽  
Ancillary Information ◽  
Biometric Information ◽  
Level Fusion

This manuscript presents a review on multibiometrics using ancillary information, in addition to the main biometric data. The proposed method involves taking non-biometric information into account in the biometric recognition process to improve system performance. This ancillary information can come from the user (the skin color), the sensor (the camera flash, etc.) or the operating environment (the ambient noise). Moreover, the paper presents an extension of the adapted sequential fusion framework through a complete description of the method used for the score-level fusion architecture presented at the IEEE BioSmart 2019 Proceedings. An optimized score-level fusion architecture is proposed. An introduction of new concepts (namely “biochemical features” and “multi origin biometrics”) is also made. The first part of the paper highlights the various biometric systems developed up to now, their architecture and characteristics. Then, the manuscript discussed about multibiometrics through its advantages, its diversity and the different levels of fusion. An attention was paid to the score-level fusion before addressing the consideration of ancillary information (or metadata) in multibiometrics. Dealing with the affective computing, the influence of emotion on the performance of biometric systems is explored. Finally, a typology of biometric adaptation is discussed. As an application, the proposed methodology will implement a multibiometric system using the face, contactless fingerprint and skin color. A single sensor will be used (a camera) with two shots while the skin color will be extracted automatically from the facial image.

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.

scholarly journals Feature-Level vs. Score-Level Fusion in the Human Identification System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Rabab A. Rasool
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Human Identification ◽  
Internet Banking ◽  
Identification System ◽  
Biometric System ◽  
Score Level Fusion ◽  
Feature Level Fusion ◽  
Level Fusion

The design of a robust human identification system is in high demand in most modern applications such as internet banking and security, where the multifeature biometric system, also called feature fusion biometric system, is one of the common solutions that increases the system reliability and improves recognition accuracy. This paper implements a comprehensive comparison between two fusion methods, named the feature-level fusion and score-level fusion, to determine which method highly improves the overall system performance. The comparison takes into consideration the image quality for the six combination datasets as well as the type of the applied feature extraction method. The four feature extraction methods, local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), principle component analysis (PCA), and Fourier descriptors (FDs), are applied separately to generate the face-iris machine vector dataset. The experimental results highlighted that the recognition accuracy has been significantly improved when the texture descriptor method, such as LBP, or the statistical method, such as PCA, is utilized with the score-level rather than feature-level fusion for all combination datasets. The maximum recognition accuracy is obtained at 97.53% with LBP and score-level fusion where the Euclidean distance (ED) is considered to measure the maximum accuracy rate at the minimum equal error rate (EER) value.

scholarly journals Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System

2019 ◽  
Vol 9 (3) ◽  
pp. 167-176 ◽  
Author(s):  
Md Wasiur Rahman ◽  
Fatema Tuz Zohra ◽  
Marina L. Gavrilova
Keyword(s):  
Logistic Regression ◽  
Gait Cycle ◽  
Feature Matching ◽  
Recognition System ◽  
Borda Count ◽  
Biometric System ◽  
K Nearest Neighbors ◽  
Score Level Fusion ◽  
Weighted Sum Method ◽  
Level Fusion

Abstract Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person’s gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi-modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank-level fusion and 96.6% for score level fusion.

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