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Hyperspectral face recognition based on sparse spectral attention deep neural networks.

Zhihua Xie, Yi Li, Jieyi Niu

|December 31, 2020

Summary

This study introduces a novel CNN framework, the sparse spectral channel-wise attention-based network (SSCANet), for hyperspectral face recognition. SSCANet effectively addresses high dimensionality and band interference, outperforming existing methods.

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Area of Science:

Computer Vision
Machine Learning
Biometrics

Background:

Convolutional Neural Networks (CNNs) excel in image classification.
Hyperspectral imaging presents challenges like high dimensionality and inter-band interference.
Suboptimal spectral bands can degrade hyperspectral face recognition performance.

Purpose of the Study:

To develop an effective CNN framework for hyperspectral face recognition.
To address challenges of high data dimensionality and spectral band selection.
To improve the accuracy and efficiency of hyperspectral face recognition.

Main Methods:

Proposed a novel CNN framework: sparse spectral channel-wise attention-based network (SSCANet).
Incorporated a channel-wise attention mechanism to recalibrate spectral bands.
Utilized a Lasso constraint strategy for adaptive spectral band selection and sparsity.

Main Results:

SSCANet adaptively emphasizes informative bands and suppresses less useful ones.
Lasso constraint promotes sparser band weights, enhancing the training process.
The proposed method demonstrated superior performance on three public hyperspectral face recognition datasets (HK-PolyU, CMU, UWA).

Conclusions:

SSCANet effectively handles hyperspectral imaging challenges in face recognition.
The adaptive band selection and attention mechanism significantly improve recognition accuracy.
The proposed framework represents a state-of-the-art advancement in hyperspectral face recognition technology.