EdgeEar: Efficient and Accurate Ear Recognition for Edge Devices

TL;DR

EdgeEar is a lightweight, hybrid CNN-transformer model that enables efficient and accurate ear recognition on resource-limited devices, achieving low error rates with significantly reduced computational costs.

Contribution

The paper introduces EdgeEar, a novel hybrid CNN-transformer architecture with low-rank approximations, reducing model size by 50 times while maintaining high accuracy for ear recognition.

Findings

EdgeEar achieves the lowest EER on UERC2023 benchmark.

Model size is reduced to below two million parameters.

Significant reduction in computational costs without sacrificing accuracy.

Abstract

Ear recognition is a contactless and unobtrusive biometric technique with applications across various domains. However, deploying high-performing ear recognition models on resource-constrained devices is challenging, limiting their applicability and widespread adoption. This paper introduces EdgeEar, a lightweight model based on a proposed hybrid CNN-transformer architecture to solve this problem. By incorporating low-rank approximations into specific linear layers, EdgeEar reduces its parameter count by a factor of 50 compared to the current state-of-the-art, bringing it below two million while maintaining competitive accuracy. Evaluation on the Unconstrained Ear Recognition Challenge (UERC2023) benchmark shows that EdgeEar achieves the lowest EER while significantly reducing computational costs. These findings demonstrate the feasibility of efficient and accurate ear recognition, which we believe will contribute to the wider adoption of ear biometrics.