Earprint Based Mobile User Authentication Using Convolutional Neural Network and SIFT

Maheshwari, M B; Arora, Sanchita; Srivastava, Akhilesh Mohan; Agrawal, Aditi; Garg, Mahak; Prakash, Surya

doi:10.1007/978-3-319-95930-6_87

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2024

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“…For just ears, the false rejection rate was reported as 7.8%. Touchscreens were used in [10] as a sensor for capturing biometric data to authenticate users on mobile devices. In general, touchscreens were not used to scan presentations on it, but in order to obtain information from the touchscreen sensor as image data, the Android kernel of mobile devices was manipulated.…”

Section: Literature Solutionsmentioning

confidence: 99%

Ear-Touch-Based Mobile User Authentication

Khiarak,

Mazaheri,

Tayebi

2024

Mathematics

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Mobile devices have become integral to daily life, necessitating robust user authentication methods to safeguard personal information. In this study, we present a new approach to mobile user authentication utilizing ear-touch interactions. Our novel system employs an analytical algorithm to authenticate users based on features extracted from ear-touch images. We conducted extensive evaluations on a dataset comprising ear-touch images from 92 subjects, achieving an average equal error rate of 0.04, indicative of high accuracy and reliability. Our results suggest that ear-touch-based authentication is a feasible and effective method for securing mobile devices.

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Section: Literature Solutionsmentioning

confidence: 99%