Design and Implementation of Novel Hybrid and Multiscale- Assisted CNN and ResNet Using Heuristic Advancement of Adaptive Deep Segmentation for Iris Recognition

IF 0.8 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
G. Babu, P. A. Khayum
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引用次数: 0

Abstract

Due to its significant applications in security, the iris recognition process has been considered as the most active research area over the last few decades. In general, the iris recognition framework has been crucially utilized for various security applications because it includes a set of features as well as does not alter its character according to the time. In recent times, emerging deep learning techniques have attained huge success, particularly in the field of the iris recognition framework model. Moreover, in considering the field of iris recognition, there is no possibility for the remarkable capability of the deep learning model as well as to attain superior performance. To handle the issues in the conventional model of iris recognition, a novel heuristic-aided deep learning framework has been implemented for recognizing the iris system. Initially, the required source iris images are gathered from the data sources. It is then followed by the pre-processing stage, where the pre-processed image is obtained. Consequently, the image segmentation process is carried out by Adaptive Deeplabv3+layers, in which the parameters are optimized using the Modified Weighted Flow Direction Algorithm (MWFDA). Finally, the iris recognition is accomplished by hybrid Hybridization of Multiscale Dilated-Assisted Learning (MDAL) that will be composed of both a Convolutional Neural Network (CNN) and a Residual Network (ResNet). To achieve optimal recognition results, the parameters in CNN and ResNet are tuned optimally by using MWFDA. The experimental results are estimated with the help of distinct measures. Contrary to conventional methods, the empirical results prove that the recommended model achieves the desired value to enhance the recognition performance.
利用启发式自适应深度分割技术,设计并实现新型混合与多萼片辅助 CNN 和 ResNet,用于虹膜识别
由于虹膜识别过程在安全领域的重要应用,它在过去几十年中一直被视为最活跃的研究领域。一般来说,虹膜识别框架已被广泛用于各种安全应用,因为它包含一系列特征,而且不会随时间改变其特征。近来,新兴的深度学习技术取得了巨大成功,尤其是在虹膜识别框架模型领域。此外,考虑到虹膜识别领域,深度学习模型不可能具有卓越的能力,也不可能取得优异的性能。为了解决传统虹膜识别模型中存在的问题,我们采用了一种新颖的启发式辅助深度学习框架来识别虹膜系统。首先,从数据源收集所需的源虹膜图像。然后进入预处理阶段,获得预处理后的图像。然后,使用自适应 Deeplabv3+layers 进行图像分割,其中使用修改加权流向算法(MWFDA)优化参数。最后,虹膜识别是通过多尺度扩张辅助学习(MDAL)的混合混合来完成的,MDAL 将由卷积神经网络(CNN)和残差网络(ResNet)组成。为了达到最佳识别效果,将使用 MWFDA 对 CNN 和 ResNet 的参数进行优化调整。实验结果借助不同的测量方法进行估算。与传统方法相反,经验结果证明,推荐的模型达到了提高识别性能的理想值。
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来源期刊
International Journal of Image and Graphics
International Journal of Image and Graphics COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
18.80%
发文量
67
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