A Method for Identification of Humans from Dorsal Hand Sub-Images Using a Siamese Network Models

Alghamdi, Mona Saleh; Pellicer, Alvaro Lopez

doi:10.2139/ssrn.4342065

Cited by 1 publication

(1 citation statement)

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“…Unimodal systems are limitations even if they are dependable and have demonstrated advantages over previously employed conventional methods. These include problems with similarity within and across classes, noise within sensed data, non-universality, susceptibility to spoo ng attempts, and non-uniformity [2]. Multimodal biometric systems require several characteristics to identify people.…”

Section: Introductionmentioning

confidence: 99%

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Amin,

Gantra

et al. 2024

Preprint

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Biometric identification technology has become a common part of daily life due to the global demand for information security and security legislation. Due to its capacity to circumvent several fundamental drawbacks of unimodal biometric systems, multimodal biometrics technology has attracted attention and grown in popularity in this respect. This research presents a novel multimodal biometric person identification system based on a VGG19 with softmax classifier (VGG19-SC) for iris and facial biometrics. The system's architecture is built on VGG19-SC, which extracts features from and categorizes images. The system was created by combining the iris and face portions of two VGG19-SC models. VGG-19 was employed to construct the well-known pertained model. A few methods, including picture augmentation and dropout techniques, were used to prevent overfitting. The VGG19-SC models were fused using feature-level and score-level fusion methods to investigate the effects of these fusion methods on recognition performance. The results demonstrated that three biometric features were more effective than two and one biometric traits in biometric identification systems. The findings similarly demonstrated the suggested method easily surpassed other cutting-edge approaches by obtaining an accuracy of 99.39% in a multi-biometric verification system.

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Section: Introductionmentioning

confidence: 99%