Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features.,The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector.,Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1.,In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

中文翻译：

---

HE-Co-HOG 和 k-SVM 分类器用于基于指关节和掌纹的多模态生物特征识别

生物特征识别系统因其在安全领域的广泛应用而成为新兴的研究领域。本研究（多模态系统）旨在寻找比单模态系统更多的应用，因为它们具有较高的用户接受度、更好的识别精度和低成本的传感器。使用指节和掌纹的生物特征识别因其独特的特征而比其他特征具有更多的应用。所提出的模型通过从掌纹和指节图像中提取的特征来执行用户身份验证。所提出的系统中的两个主要过程是特征提取和分类。所提出的模型在预处理后使用所提出的 HE-Co-HOG 模型从掌纹和指关节中提取特征。提出的 HE-Co-HOG 模型找到掌纹 HE-Co-HOG 向量和指关节 HE-Co-HOG 向量。这些来自掌纹和指节的特征与分数萤火虫 (FFF) 算法的最佳权重得分相结合。分层k-SVM分类器从融合向量中对每个人的身份进行分类。使用掌纹和指节图像的两个标准数据集进行模拟。仿真结果从两个方面进行分析。在第一种方法中，HE-Co-HOG 向量的 bin 大小因数据集的各种训练而异。在第二种方法中，针对不同训练规模的数据集，将所提出模型的性能与现有模型进行了比较。从仿真结果来看，所提出的模型的最大精度为0。95 和最低的错误接受率和错误拒绝率，值为 0.1。,本文开发了基于提出的 HE-Co-HOG 与 k-SVM 和 FFF 的多模态生物识别系统。所提出的模型使用掌纹和指关节图像作为生物特征。提出的 HE-Co-HOG 向量的开发是通过使用全熵权重修改 Co-HOG 来完成的。