Novel Canonical Correlation Analysis Based Feature Level Fusion Algorithm for Multimodal Recognition in Biometric Sensor Systems

Kamlaskar, Chetana; Deshmukh, Shubhangi; Gosavi, Suresh; Abhyankar, Aditya

doi:10.1166/sl.2019.4013

Cited by 12 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sadhya and Raman [49] 0.105 Punithavathi et al [45] 1.9 Morampudi et al [46] 0.39 Lai et al [51] 0.54 Mahesh Kumar et al [48] 0.13 Dwivedi et al [52] 0.43 Morampudi et al [50] 0.31 CMAQF 0.15 Kamalskar et al [53] 2.5947…”

Section: Casia-v3-intervalmentioning

confidence: 99%

Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter

Arepalli,

Boobalan

2024

View full text Add to dashboard Cite

Biometric-based authentication systems (BAS) can provide a strong security guarantee regarding the identity of users over traditional authentication systems. The iris of the eye is widely acknowledged as one of the most robust biometrics due to its exceptional performance. Despite this, templates used in traditional iris recognition systems remain unprotected, rendering them highly susceptible to various security and privacy breaches. However, several cancelable biometric schemes being introduced but at the expense of substantially decreased accuracy performance and increased computational time. To address this, we propose a cancelable multi-instance iris authentication system utilizing a quotient filter (CMAQF). The purpose of the quotient filter in CMAQF is to distort the biometric information without compromising the accuracy. Modified local random projection is applied on the fused iris template to generate the reduced template results in less authentication time. Experiments have been conducted on publicly available iris databases to assess the efficiency of CMAQF. The experimental results conclude that CMAQF achieves reasonable performance compared to existing methods, satisfying the properties of irreversibility, diversity, and revocability.

show abstract

Section: Casia-v3-intervalmentioning

confidence: 99%

Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter

Arepalli,

Boobalan

2024

View full text Add to dashboard Cite

show abstract

“…The first one is based on matrix transformation strategies, such as Principal Component Analysis (PCA) [15], enhanced partial discrete Fourier transform [16], and weighted joint sparse representation-based classification (WJSRC) [17]. As a further improvement of the matrix fusion strategy, Canonical Correlation Analysis (CCA) [18,19] maximizes the correlation of different modal information by iteration to derive a linear mapping matrix, which further maps the separated different modal features into the same common space. Discriminant Correlation Analysis (DCA) [7] and Adversarial Canonical Correlation Analysis (ACCA) [20] have been applied to biometrics as variants of the above methods.…”

Section: A Multimodal Biometric Recognitionmentioning

confidence: 99%

Multimodal Finger Recognition Based on Asymmetric Networks With Fused Similarity

Huang

Wang

2023

IEEE Access

View full text Add to dashboard Cite

Multimodal biometric system has received increasing interest as it offers a more secure and accurate authentication solution than unimodal systems. However, existing biometric fusion methods are still inadequate in dealing with correlations and redundancy of multimodal features simultaneously, causing bottlenecks in performance improvement. To overcome the above problem, this paper proposes an end-toend multimodal finger recognition model that incorporates attention mechanisms into a similarity-aware encoder to achieve accurate recognition results for accurate identification results. Firstly, due to the different distribution of fingerprint and finger vein images, we propose a finger asymmetric backbone network (FAB-Net) for extracting discriminative intra-modal features, which reduces the network width by efficient utilization of feature maps. Then, a novel attention-based encoder fusion network (AEF-Net) with fused similarity performs dimensionality reduction-based fusion on multimodal multilevel features to alleviate performance degradation due to information redundancy. We also introduce channel attention in AEF-Net, which differs from the traditional attention mechanism by considering interdependencies between modalities to further improve performance. Extensive recognition experiments are conducted on three multimodal finger databases to verify the effectiveness of our method compared to state-of-the-art methods. Detailed ablation studies have also been carried out, which demonstrated that encoder-based reconstruction of redundant information can improve recognition performance.INDEX TERMS Multimodal biometric recognition, feature fusion, autoencoder, deep learning.

show abstract

“…Multiple features are usually fused to achieve complementary description of the target and improve recognition accuracy. Traditional target fusion recognition methods include weighted average, canonical correlation analysis (CCA) [12], multi-kernel learning (MKL) [13], the Dempster-Shafer (DS) evidence theory [14], Bayesian inference [15], etc. These methods are theoretically mature and have been widely used in engineering, but they heavily rely on high-precision extraction of features.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Zhang,

Rao,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targets and decoys. However, most existing methods ignore the fuzziness of multi-dimensional features, and their performance mainly depends on the accuracy of feature extraction, with certain limitations in handling uncertainty and noise. This article proposes a space IR dim target fusion recognition method, which is based on fuzzy comprehensive of spatio-temporal correlation. First, we obtained multi-dimensional IR features of the target through multi-time and multi-spectral detectors, then we established and calculated the adaptive fuzzy-membership function of the features. Next, we applied the entropy weight method to ascertain the objective fusion weights of each feature and computed the spatially fuzzified fusion judgments for the targets. Finally, the fuzzy comprehensive function was used to perform temporal recursive judgment, and the ultimate fusion recognition result was obtained by integrating the results of each temporal recursive judgment. The simulation and comparative experimental results indicate that the proposed method improved the accuracy and robustness of IR dim target recognition in complex environments. Under ideal conditions, it can achieve an accuracy of 88.0% and a recall of 97.5% for the real target. In addition, this article also analyzes the impact of fusion feature combinations, fusion frame counts, different feature extraction errors, and feature database size on recognition performance. The research in this article can enable space-based IR detection systems to make more accurate and stable decisions, promoting defense capabilities and ensuring space security.

show abstract

Novel Canonical Correlation Analysis Based Feature Level Fusion Algorithm for Multimodal Recognition in Biometric Sensor Systems

Cited by 12 publications

References 0 publications

Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter

Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter

Multimodal Finger Recognition Based on Asymmetric Networks With Fused Similarity

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Contact Info

Product

Resources

About