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Multimodal hybrid features in 3D ear recognition

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Abstract

Despite being the most rapidly evolving biometric trait, the ear suffers from a few drawbacks, such as being affected by posture, illumination, and scaling in the two-dimensional domain. To address these issues, researchers focused on the 3D domain, as the intrinsic features of the 3D ear have significantly contributed to performance enhancement. However, it has also been observed that combining 2D and 3D ear features improves recognition better than using 3D ear features alone. This article presents a hybrid descriptor where the feature vectors for ear recognition are derived from the multimodal ear using classical and learning-based approaches. The classical approach generates features using a covariance matrix, whereas the learning-based approach uses a deep auto-encoder to generate features. Further, these features are combined to create a hybrid descriptor. Thorough experiments were carried out on the largest publicly accessible ear database to demonstrate the effectiveness of the proposed approach and compare its performance to that of state-of-the-art techniques.

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Authors and Affiliations

  1. R.M.D. Engineering College, Tamil Nadu, 601206, India

    Karthika Ganesan & Chilambuchelvan A

  2. C2PS & KUCARS, Khalifa University, Abu Dhabi, 127788, UAE

    Iyyakutti Iyappan Ganapathi, Sajid Javed & Naoufel Werghi

Authors
  1. Karthika Ganesan
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  2. Chilambuchelvan A
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  3. Iyyakutti Iyappan Ganapathi
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  4. Sajid Javed
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  5. Naoufel Werghi
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Correspondence to Karthika Ganesan.

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Ganesan, K., A, C., Ganapathi, I.I. et al. Multimodal hybrid features in 3D ear recognition. Appl Intell 53, 11618–11635 (2023). https://doi.org/10.1007/s10489-022-04071-5

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