scholarly journals Signal‐level fusion for indexing and retrieval of facial biometric data

2022 ◽  
Author(s):  
Pawel Drozdowski ◽  
Fabian Stockhardt ◽  
Christian Rathgeb ◽  
Christoph Busch
Keyword(s):  
Biometric Data ◽  
Indexing And Retrieval ◽  
Level Fusion

scholarly journals Analyzing the Effects of Data Augmentation on Single and Multimodal Biometrics

2020 ◽  
Vol 39 (3) ◽  
pp. 647-656
Author(s):  
Mehwish Leghari ◽  
Shahzad Memon ◽  
Lachman Das Dhomeja ◽  
Akhter Hussain Jalbani
Keyword(s):  
Data Augmentation ◽  
Research Study ◽  
Real Data ◽  
Fingerprint Recognition ◽  
Multimodal Biometrics ◽  
Biometric Data ◽  
Augmentation Techniques ◽  
Signature Recognition ◽  
Using Data ◽  
Level Fusion

Now-a-days, in the field of machine learning the data augmentation techniques are common in use, especially with deep neural networks, where a large amount of data is required to train the network. The effectiveness of the data augmentation technique has been analyzed for many applications; however, it has not been analyzed separately for the multimodal biometrics. This research analyzes the effects of data augmentation on single biometric data and multimodal biometric data. In this research, the features from two biometric modalities: fingerprint and signature, have been fused together at the feature level. The primary motivation for fusing biometric data at feature level is to secure the privacy of the user’s biometric data. The results that have been achieved by using data augmentation are presented in this research. The experimental results for the fingerprint recognition, signature recognition and the feature-level fusion of fingerprint with signature have been presented separately. The results show that the accuracy of the training classifier can be enhanced with data augmentation techniques when the size of real data samples is insufficient. This research study explores that how the effectiveness of data augmentation gradually increases with the number of templates for the fused biometric data by making the number of templates double each time until the classifier achieved the accuracy of 99%.

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.

Impact of Biometric Data Quality on Rank-Level Fusion Schemes

2013 ◽  
pp. 209-216 ◽  
Author(s):  
Emanuela Marasco ◽  
Ayman Abaza ◽  
Luca Lugini ◽  
Bojan Cukic
Keyword(s):  
Data Quality ◽  
Biometric Data ◽  
Level Fusion

scholarly journals Performance Evaluation of Multimodal Multifeature Authentication System UsingKNN Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gayathri Rajagopal ◽  
Ramamoorthy Palaniswamy
Keyword(s):  
Recognition Accuracy ◽  
Principal Component ◽  
Biometric System ◽  
Biometric Data ◽  
Score Level Fusion ◽  
Feature Level Fusion ◽  
Rich Information ◽  
Authentication System ◽  
Matching Score ◽  
Level Fusion

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Audiovisual Expression of Biometric Data Based on the Polysomnography Test

2018 ◽  
Vol 5 (4) ◽  
pp. 1-5
Author(s):  
Na Yea Oh ◽  
Hee Soo Kim ◽  
Jin Wan Park
Keyword(s):  
Biometric Data
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