Data Augmentation for Small Sample Iris Image Based on a Modified Sparrow Search Algorithm

• Published in: International journal of computational intelligence systems Vol. 15; no. 1; pp. 1 - 11
• Main Authors: Xiong, Qi, Zhang, Xinman, He, Shaobo, Shen, Jun
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 16-12-2022; Springer
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Data augmentation; Engineering; Iris images; Mathematical Logic and Foundations; Mechatronics; Research Article; Robotics; Small sample; Sparrow search algorithm; Swarm intelligence; Iris images; Data augmentation; Small sample; Swarm intelligence; Sparrow search algorithm
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-022-00173-7

Training convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small sample iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-Iris-Thousand dataset and test the proposed augmentation method on four CNN models. The experimental results show that, compared with the some standard image augmentation methods such as flipping, mirroring and clipping, the accuracy and Equal Error Rate (EER)of the proposed method have been significantly improved. The accuracy and EER of the CNN models with the best recognition performance can reach 95.5 and 0.6809 respectively. This fully shows that the data augmentation method proposed in this paper is effective and quite simple to implement.