Ear Biometrics and Recognition Techniques

Ear biometrics exploits the unique morphology of the external ear to verify or identify individuals. The outer ear presents a stable, distinctive pattern of ridges, curves and lobes that remains largely unchanged after adolescence, making it an attractive modality for security, forensic and medical applications. A typical ear recognition system comprises four stages: automated detection and localisation, geometric normalisation to compensate for pose and scale, feature extraction to convert ear geometry or texture into a discriminative representation, and matching against a database of enrolled subjects. Early approaches relied on handcrafted descriptors such as Gabor filters, local binary patterns or kernel-based discriminant analysis. Recent advances have shifted towards deep convolutional networks which learn robust features directly from image data, enabling high performance even under variations in illumination, occlusion and head orientation. Complementary 3D scanning techniques capture surface shape with greater fidelity, facilitating recognition under partial occlusion or non-cooperative capture. Together, these developments have extended the reach of ear recognition to border control, mobile authentication and clinical diagnosis of auricular deformities, demonstrating the global significance and practical utility of this biometric modality.

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Ear Biometrics and Recognition Techniques publication trend

The graph below shows the total number of articles in ear biometrics and recognition techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Biometrics: The science of automated recognition of individuals based on physiological or behavioural characteristics.

Convolutional Neural Network (CNN): A class of deep learning models specialised for grid-structured data, particularly images, that learn hierarchical feature representations.

Transfer Learning: A method for adapting a pretrained model on one task to perform a related task, often requiring fewer labelled samples.

Ensemble Learning: The practice of combining predictions from multiple models to improve overall accuracy and robustness.

Grad-CAM (Gradient-weighted Class Activation Mapping): A technique that produces visual explanations by highlighting regions in an input image most influential to a network’s prediction.

References

1. Ear Recognition Based on Gabor Features and KFDA. The Scientific World JOURNAL (2014).
2. Deep Convolutional Neural Networks for Unconstrained Ear Recognition. IEEE Access (2020).
3. Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition. Sensors (2019).
4. Towards Explainable Ear Recognition Systems Using Deep Residual Networks. IEEE Access (2021).
5. 3D Ear Normalization and Recognition Based on Local Surface Variation. Applied Sciences (2017).
6. Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition. Symmetry (2018).