Open Access

ARTICLE

DeepBio: A Deep CNN and Bi-LSTM Learning for Person Identification Using Ear Biometrics

Anshul Mahajan^*, Sunil K. Singla

Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, 147004, India

* Corresponding Author: Anshul Mahajan. Email:

(This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)

Computer Modeling in Engineering & Sciences 2024, 141(2), 1623-1649. https://doi.org/10.32604/cmes.2024.054468

Received 29 May 2024; Accepted 31 July 2024; Issue published 27 September 2024

Abstract

The identification of individuals through ear images is a prominent area of study in the biometric sector. Facial recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing, prompting the exploration of supplementary biometric measures such as ear biometrics. The research proposes a Deep Learning (DL) framework, termed DeepBio, using ear biometrics for human identification. It employs two DL models and five datasets, including IIT Delhi (IITD-I and IITD-II), annotated web images (AWI), mathematical analysis of images (AMI), and EARVN1. Data augmentation techniques such as flipping, translation, and Gaussian noise are applied to enhance model performance and mitigate overfitting. Feature extraction and human identification are conducted using a hybrid approach combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). The DeepBio framework achieves high recognition rates of 97.97%, 99.37%, 98.57%, 94.5%, and 96.87% on the respective datasets. Comparative analysis with existing techniques demonstrates improvements of 0.41%, 0.47%, 12%, and 9.75% on IITD-II, AMI, AWE, and EARVN1 datasets, respectively.

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Keywords

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