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. 2022 Nov 20;22(22):8988.
doi: 10.3390/s22228988.

Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model

Md Mursalin  1 , Mohiuddin Ahmed  1 , Paul Haskell-Dowland  1
Affiliations

Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model

Md Mursalin et al. Sensors (Basel). .

Abstract

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.

Keywords: 3D morphable model; detection; ear biometrics; recognition.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Various biometric traits for human recognition.
Figure 2
Figure 2
Face recognition challenges. All of these images (same person) are taken within 2–3 years.
Figure 3
Figure 3
Block diagram of the proposed ear recognition method.
Figure 4
Figure 4
Identification rate on the UND J2 dataset.
Figure 5
Figure 5
Performance evaluation of the proposed method on the UND J2 dataset using the ROC curve.
Figure 6
Figure 6
Examples of occlusions by earrings.
See this image and copyright information in PMC

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