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Anatomy of the Ear

Anatomy of the Ear

Auditory sensation, commonly called hearing, involves the transformation of sonic waves into neural impulses facilitated by the structures of the auditory organ. The prominent, flesh-like structure on the side of the head, called the auricle, directs sound waves towards the auditory canal. The auricle is often mislabeled as the pinna, a term more aligned with mobile structures like a feline's external ear. The auditory canal penetrates the cranium via the external auditory meatus of the...

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Updated: May 1, 2026

A Method to Study Adaptation to Left-Right Reversed Audition

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

3D ear identification based on sparse representation.

Lin Zhang¹, Zhixuan Ding¹, Hongyu Li¹

¹School of Software Engineering, Tongji University, Shanghai, China.

|April 18, 2014

Summary

This summary is machine-generated.

This study introduces a novel 3D ear identification system using sparse representation classification. The method offers an effective and efficient solution for automatic personal authentication via 3D ear biometrics.

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Area of Science:

Computer Science
Biometrics
Pattern Recognition

Background:

Biometrics offer high-confidence personal authentication.
3D ear shape is a promising biometric trait due to rich features and ease of acquisition.
Existing Iterative Closest Point (ICP)-based methods struggle with one-to-many 3D ear identification.

Purpose of the Study:

To develop a novel, fully automatic 3D ear identification system.
To address the inefficiency of current methods in one-to-many identification scenarios.
To introduce sparse representation classification to 3D ear identification.

Main Methods:

A template-based method for accurate and efficient 3D ear detection.
Representation of extracted ear regions in a canonical coordinate system.
Feature extraction using a Principal Component Analysis (PCA)-based local descriptor.
Classification using sparse representation classification (l1-minimization).

Main Results:

The proposed system demonstrates effectiveness and efficiency in 3D ear identification.
The template-based detection and PCA features facilitate subsequent stages.
Sparse representation classification proves suitable for this biometric task.
Experimental results on a benchmark dataset validate the approach.

Conclusions:

The developed system provides an effective and efficient solution for 3D ear identification.
This work is the first to apply sparse representation to 3D ear identification.
The proposed method advances automatic personal authentication using 3D ear biometrics.