Image enhancement optimization of resnet convolutional neural network for palmprint recognition

Yunfei Qu

doi:10.1117/12.3033501

13 June 2024 Image enhancement optimization of resnet convolutional neural network for palmprint recognition

Yunfei Qu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131804S (2024) https://doi.org/10.1117/12.3033501
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Palmprint recognition serves as a biometric identification technology, involving the analysis and comparison of ridge patterns on an individual's palm for identity verification or individual recognition. This technique relies on the unique patterns present on the skin of each person's palm, encompassing features such as wrinkles, grooves, and skin texture. Utilizing image processing techniques and convolutional neural networks (CNNs), palmprint features can be extracted, and classification tasks can be achieved even with a limited training dataset. This study explores the impact of various data augmentation methods on the improvement of classification accuracy for ResNet convolutional neural networks. Experimenting with alterations in brightness, contrast, noise addition, and image flipping, as well as exploring combinations of these augmentation techniques, resulted in diverse experimental outcomes. Significantly, the repetitive adjustment of brightness and contrast, along with their combined effects, notably contributed to enhancing accuracy in the ResNet convolutional neural network.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunfei Qu "Image enhancement optimization of resnet convolutional neural network for palmprint recognition", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131804S (13 June 2024); https://doi.org/10.1117/12.3033501

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KEYWORDS

Data modeling

Education and training

Convolutional neural networks

Image processing

Pattern recognition

Image enhancement

Biometrics

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