In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the performance of the multimodal contactless biometric pattern recognition system. The most significant advantage of palm vein and palm dorsal vein recognition is that it provides relatively high accuracy, reliability and also liveness detection. In this recognition process, the palm and its dorsal vein images are segmented using multilevel segmentation of region of interest (ROI) depending on Adaptive Hyper Kernel Optimized Fuzzy Clustering Technique (Adapt-HKFCT), and then ROI extracted based on the valley points among the index and ring finger using the Knuckle points. Then, the features of the ROI amid the index and ring finger are extracted using centre-symmetric local binary pattern algorithm and fed to the Chaos-DNN for training and classification. The efficiency of the features is optimized using SPOA. The extracted feature points help in authenticating a person using his/her chosen biometric traits for security purposes. The objective of this system is to increase the efficiency of the biometric system, and the performance is calculated depending on equal error rate, correct recognition rate, decidability index, false acceptance rate and false rejection rate. The proposed algorithm has been implemented in MATLAB platform. The experimental outcomes demonstrate that the proposed palm vein recognition depending Adapt-HKFCT with Chaos-DNN using SPOA method (PVR-AHKFCT-CDNN-SPOA) attains higher performance metrics with an accuracy of 98.3% and is also more efficient likened to the other existing methods, such as K Nearest Neighbour with Particle Swarm Optimization (PVR-KNN-PSO), Fuzzy Brain Storm Optimization based on Adaptive Thresholding (PVR-FBSO-AT), multiscale local binary pattern with ant colony optimization (PVR-MSLBP-ACO) and Deep Learning with Bayesian Optimization (PVR-DL-BO).

中文翻译：

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一种使用 Adapt-HKFCT 分割和基于特征的混沌集成深度神经网络 (Chaos-DNN-SPOA) 的多模式架构，用于非接触式生物特征手掌静脉识别系统

最近，各种单峰生物特征的融合越来越受到致力于使用实际生物特征的研究人员的关注。在本文中，提出了一种使用 Sandpiper 优化算法 (SPOA) 的混沌集成深度神经网络 (Chaos-DNN)，以提高多模态非接触式生物特征模式识别系统的性能。手掌静脉和手掌背静脉识别最显着的优点是它提供了相对较高的准确性、可靠性以及活体检测。在此识别过程中，根据自适应超核优化模糊聚类技术 (Adapt-HKFCT)，使用感兴趣区域 (ROI) 的多级分割对手掌及其背静脉图像进行分割，然后使用Knuckle点根据食指和无名指之间的谷点提取ROI。然后，使用中心对称局部二值模式算法提取食指和无名指中的 ROI 特征，并馈送到 Chaos-DNN 进行训练和分类。使用 SPOA 优化了功能的效率。出于安全目的，提取的特征点有助于使用他/她选择的生物特征来验证一个人。该系统的目标是提高生物识别系统的效率，其性能是根据相等错误率、正确识别率、可判定性指标、错误接受率和错误拒绝率来计算的。所提出的算法已在 MATLAB 平台上实现。