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نتیجه جستجو - یادگیری ویژگی مشترک

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Jointly learning multi-instance hand-based biometric descriptor
توصیف کننده بیومتریک مبتنی بر دست چند نمونه ای را یادگیری ارتباطی-2021
Multibiometric recognition has become one of the most important solutions for enhancing overall personal recognition performance due to several inherent limitations of unimodal biometrics, such as nonuniversality and unacceptable reliability. However, most existing multibiometrics fuse completely different biometric traits based on addition schemes, which usually require several sensors and make the final feature sets large. In this paper, we propose a joint multi-instance hand-based biometric feature learning method for bio- metric recognition. Specifically, we first exploit the important direction data from multi- instance biometric images. Then, we simultaneously learn the discriminative features of multi-instance biometric traits and exploit the collaborative representations of multi- instance biometric features such that the final joint multi-instance feature descriptor is compact. Moreover, the importance weights of different biometric instances can be adaptively learned. Experimental results on the baseline multi-instance finger-knuckle-print and palmprint databases demonstrate the promising effectiveness of the proposed method.© 2021 Elsevier Inc. All rights reserved.
Keywords: Multibiometrics | Multi-instance biometric recognition | Joint feature learning | Compact feature representation
مقاله انگلیسی
2 Joint discriminative feature learning for multimodal finger recognition
یادگیری ویژگی های تبعیض آمیز مشترک برای تشخیص انگشتان چند حالته-2021
Recently, finger-based multimodal biometrics, due to its high security and stability, has received considerable attention compared with unimodal biometrics. However, existing multimodal finger feature ex- traction approaches separately extract the features of different modalities, at the same time ignoring correlations among these different modalities. Furthermore, most of the conventional finger feature representation approaches are hand-crafted by design, which require strong prior knowledge. It is therefore very important to explore and develop a suitable feature representation and fusion strategy for mul- timodal biometrics recognition. In this paper, we proposed a joint discriminative feature learning (JDFL) framework for multimodal finger recognition by combining finger vein (FV) and finger knuckle print (FKP) patterns. For the FV and FKP images, we first established the informative dominant direction vector by convoluting a bank of Gabor filters and the original finger image. Then, we developed a simple yet effective feature learning algorithm, which simultaneously maximized the distance of between-class samples and minimized the distance of within-class samples, as well as maximized the correlation among inter- modality samples of the within-class. Finally, we integrated the block-wise histograms of the learned feature maps together for multimodal finger fusion recognition. Experimental results demonstrated that the proposed approach has a better recognition performance than state-of-the-art finger recognition methods.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Multimodal biometrics | Feature fusion | Inter-modality | Joint feature learning
مقاله انگلیسی
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