The study investigates the benefits of using Complex Structure Tensor (CST) theory to improve the performance of Convolutional Neural Networks (CNNs) in the context of periocular biometric recognition. CST provides a compact representation of the local power spectrum, encoding the presence and orientation of texture patterns in the image.
The key highlights are:
Experiments show that CNNs struggle to effectively extract orientation features from grayscale images alone. Providing the CST features, which include magnitude, angle, and confidence of the dominant texture orientations, as input to CNNs consistently improves identification accuracy compared to using grayscale inputs.
The CST features are obtained using a mini complex convolutional network, which is more efficient than using a full Gabor filter bank. This allows for network compression without compromising performance.
The proposed method generalizes across different CNN architectures, including ResNet50, DenseNet121, Xception, InceptionV3, and MobileNetV2, outperforming the baseline grayscale versions in most cases.
Experiments were conducted on two publicly available periocular datasets, Cross-Eyed and PolyU, in both near-infrared and visible spectra. The results demonstrate the effectiveness and generalization of the proposed approach.
Compared to previous state-of-the-art methods, the CST-enhanced CNNs achieve comparable or better performance, especially in the more challenging Open-World protocol, while requiring no pre-training on external datasets.
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by Kevin Hernan... at arxiv.org 04-25-2024
https://arxiv.org/pdf/2404.15608.pdfDeeper Inquiries