A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

Mehraj Haider; Mir Ajaz Hussain

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A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

机译：A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

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摘要

In a multimodal biometric recognition system, the integration of multiple features derived from various biometric modalities seeks to overcome the several drawbacks found in a unimodal biometric system. In this paper, we have proposed a novel multimodal biometric recognition system based on a multi-level hybrid feature fusion mechanism to compact knowledge from multiple feature vectors. Several pre-trained networks with transfer learning, namely AlexNet, Inceptionv2, Densenet201, Resnet101, and Resnet-Inceptionv2, are employed to extract feature vectors to fuse with handcrafted feature vectors based on HOG feature descriptor. Canonical correlation analysis (CCA) and Discriminant Correlation Analysis (DCA) are utilized at a multi-level hybrid mechanism. To test the proposed framework, we used three biometric features: Ear, Face, and Gait. Numerical results have proved that our model outperformed other state of the art recent variants.

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《Herald of the Russian Academy of Sciences》 |2021年第2期|176-196|共21页
作者
Mehraj Haider; Mir Ajaz Hussain;
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作者单位

Natl Inst Technol Srinagar;

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原文格式 PDF
正文语种英语
中图分类自然科学总论;
关键词
multi-biometric system; feature level fusion; multi-level feature fusion; CNN; HOG features; SCORE-LEVEL FUSION; FACE RECOGNITION; EAR; DESCRIPTORS; ROBUST; VOICE; VEIN; ECG;

A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

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