Deep Learning Technique for Fingerprint Recognition

Aboalhsan, Abdulmajed; Alatawi, Mohammed Naif

doi:10.1109/iccit52419.2022.9711634

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…One of the most robust, accurate, and secure biometric identification methods is gait, which can identify subjects from a distance based on fundamental dynamic patterns to identify the same person across several fixed cameras [1]. Compared to other commonly used biometrics modalities, such as fingerprint [2], iris [3], face [4], and DNA [5], the gait feature offers unique advantages, which are noninvasive, hard to disguise, sturdy to low-resolution images, and not need cooperative from a subject [1]. Due to the potential and advantages of gait identification considered algorithms than other biometrics, it uses in a wide range of security applications concerning hospitals, trade malls, banks, military installations, airports, religious institutions, etc.…”

Section: Introductionmentioning

confidence: 99%

Model-based and model-free deep features fusion for high performed human gait recognition

Yousef¹,

Khalil

Samra

et al. 2023

J Supercomput

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In the last decade, the need for a non-contact biometric model for recognizing candidates has increased, especially after the pandemic of COVID-19 appeared and spread worldwide. This paper presents a novel deep convolutional neural network (CNN) model that guarantees quick, safe, and precise human authentication via their poses and walking style. The concatenated fusion between the proposed CNN and a fully connected model has been formulated, utilized, and tested. The proposed CNN extracts the human features from two main sources: (1) human silhouette images according to model-free and (2) human joints, limbs, and static joint distances according to a model-based via a novel, fully connected deep-layer structure. The most commonly used dataset, CASIA gait families, has been utilized and tested. Numerous performance metrics have been evaluated to measure the system quality, including accuracy, specificity, sensitivity, false negative rate, and training time. Experimental results reveal that the proposed model can enhance recognition performance in a superior manner compared with the latest state-of-the-art studies. Moreover, the suggested system introduces a robust real-time authentication with any covariate conditions, scoring 99.8% and 99.6% accuracy in identifying casia (B) and casia (A) datasets, respectively.

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