A finger biometric system at an unconstrained environment is presented in this paper. A technique for hand image normalization is implemented at the preprocessing stage that decomposes the main hand contour into finger-level shape representation. This normalization technique follows subtraction of transformed binary image from binary hand contour image to generate the left-side of finger profiles (LSFPs). Then, XOR is applied to LSFP image and hand contour image to produce the right side of finger profiles. During feature extraction, initially, 30 geometric features are computed from every normalized finger. The rank-based forward–backward greedy algorithm is followed to select relevant features and to enhance classification accuracy. Two different subsets of features containing 9 and 12 discriminative features per finger are selected for two separate experimentations those use the ${k}$ -nearest neighbor and the random forest (RF) for classification on the Bosphorus hand database. The experiments with the selected features of four fingers except the thumb have obtained improved performances compared to features extracted from five fingers and also other existing methods evaluated on the Bosphorus database. The best identification accuracies of 96.56% and 95.92% using the RF classifier have been achieved for the right- and left-hand images of 638 subjects, respectively. An equal error rate of 0.078 is obtained for both types of the hand images.

中文翻译：

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使用来自手指几何轮廓的选定特征进行人体识别

本文介绍了一种无约束环境下的手指生物识别系统。在预处理阶段实施了一种手部图像归一化技术，该技术将主要手部轮廓分解为手指级形状表示。这种归一化技术遵循从二进制手轮廓图像中减去变换后的二进制图像以生成手指轮廓 (LSFP) 的左侧。然后，将异或应用于 LSFP 图像和手部轮廓图像以生成手指轮廓的右侧。在特征提取期间，最初从每个归一化的手指计算 30 个几何特征。遵循基于等级的前向-后向贪婪算法来选择相关特征并提高分类精度。每个手指包含 9 个和 12 个判别特征的两个不同特征子集被选择用于两个单独的实验，这些实验使用 ${k}$ -最近邻和随机森林 (RF) 在博斯普鲁斯海峡手部数据库上进行分类。与从五个手指提取的特征以及在博斯普鲁斯海峡数据库上评估的其他现有方法相比，使用除拇指以外的四个手指的选定特征进行的实验获得了改进的性能。使用 RF 分类器对 638 个对象的右手和左手图像分别实现了 96.56% 和 95.92% 的最佳识别准确率。两种类型的手部图像均获得 0.078 的相同错误率。与从五个手指提取的特征以及在博斯普鲁斯海峡数据库上评估的其他现有方法相比，使用除拇指以外的四个手指的选定特征进行的实验获得了改进的性能。使用 RF 分类器对 638 个对象的右手和左手图像分别实现了 96.56% 和 95.92% 的最佳识别准确率。两种类型的手部图像均获得 0.078 的相同错误率。与从五个手指提取的特征以及在博斯普鲁斯海峡数据库上评估的其他现有方法相比，使用除拇指以外的四个手指的选定特征进行的实验获得了改进的性能。使用 RF 分类器对 638 个对象的右手和左手图像分别实现了 96.56% 和 95.92% 的最佳识别准确率。两种类型的手部图像均获得 0.078 的相同错误率。分别。两种类型的手部图像均获得 0.078 的相同错误率。分别。两种类型的手部图像均获得 0.078 的相同错误率。