Biometric systems depending on ‘one-modal biometrics’ do not meet up with the required performance necessities for huge user appliances, owing to certain issues like ‘noisy data, intra-class variations, restricted degrees of freedom, spoof attacks and unacceptable error rates’. This work tends to discover a multimodal biometric recognition (MBR) model that includes three main phases like ‘(i) pre-processing, (ii) segmentation, (iii) feature extraction and (iv) classification’. Initially, the images are pre-processed and those pre-processed images are subjected to segmentation. In this context, segmentation is carried out using the Otsu thresholding model. The segmented images are then subjected to a feature extraction process. This work exploits local feature extraction, where ‘Gabor filter features, Zernibe moment features and proposed local binary pattern features’ are extracted. Subsequently, the fusion framework is developed, which has enhanced classification abilities with minimal dimension for MBR. As the next process, recognition takes place by the optimized neural network (NN) model. As a novelty, the training of NN is carried out using a new modified dragonfly algorithm by selecting the optimal weight. Finally, analysis is carried out for validating the betterment of the presented model in terms of different measures.

中文翻译：

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具有局部融合视觉特征的多模态生物特征识别的元启发式人工神经网络框架

由于“噪声数据、类内变化、自由度受限、欺骗攻击和不可接受的错误率”等某些问题，依赖于“单模态生物识别技术”的生物识别系统无法满足大型用户设备所需的性能要求. 这项工作倾向于发现一种多模态生物特征识别 (MBR) 模型，该模型包括三个主要阶段，如“(i) 预处理、(ii) 分割、(iii) 特征提取和 (iv) 分类”。最初，对图像进行预处理，并对这些预处理图像进行分割。在这种情况下，使用 Otsu 阈值模型进行分割。然后对分割后的图像进行特征提取处理。这项工作利用局部特征提取，其中“Gabor 过滤器特征，提取 Zernibe 矩特征和提出的局部二值模式特征。随后，开发了融合框架，该框架增强了 MBR 最小维度的分类能力。作为下一个过程，通过优化的神经网络 (NN) 模型进行识别。作为一种新颖性，通过选择最佳权重，使用一种新的修改过的蜻蜓算法对神经网络进行训练。最后，进行分析以验证所提出的模型在不同措施方面的改进。神经网络的训练是使用一种新的改进的蜻蜓算法通过选择最佳权重来进行的。最后，进行分析以验证所提出的模型在不同措施方面的改进。神经网络的训练是使用一种新的改进的蜻蜓算法通过选择最佳权重来进行的。最后，进行分析以验证所提出的模型在不同措施方面的改进。