Open Access

ARTICLE

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Raniyah Wazirali¹, Rami Ahmed^2,*

1 College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
2 The School of Information Technology, Sebha University, Sebha, 71, Libya

* Corresponding Author: Rami Ahmed. Email:

Computer Systems Science and Engineering 2022, 41(1), 305-320. https://doi.org/10.32604/csse.2022.020504

Received 26 May 2021; Accepted 27 June 2021; Issue published 08 October 2021

Abstract

The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.

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Citations