Biometrics is indispensable in this modern digital era for secure automated human authentication in various fields of machine learning and pattern recognition. Hand geometry is a promising physiological biometric trait with ample deployed application areas for identity verification. Due to the intricate anatomic foundation of the thumb and substantial inter-finger posture variation, satisfactory performances cannot be achieved while the thumb is included in the contact-free environment. To overcome the hindrances associated with the thumb, four finger-based (excluding the thumb) biometric approaches have been devised. In this chapter, a four-finger based biometric method has been presented. Again, selection of salient features is essential to reduce the feature dimensionality by eliminating the insignificant features. Weights are assigned according to the discriminative efficiency of the features to emphasize on the essential features. Two different strategies namely, the global and local feature selection methods are adopted based on the adaptive forward-selection and backward-elimination (FoBa) algorithm. The identification performances are evaluated using the weighted k-nearest neighbor (wk-NN) and random forest (RF) classifiers. The experiments are conducted using the selected feature subsets over the 300 subjects of the Bosphorus hand database. The best identification accuracy of 98.67%, and equal error rate (EER) of 4.6% have been achieved using the subset of 25 features which are selected by the rank-based local FoBa algorithm.

翻译：生物特征识别技术在现代数字化时代不可或缺，用于在机器学习和模式识别的各个领域实现安全自动化的人类身份认证。手部几何形状是一种前景广阔的生理生物特征，已在身份验证领域得到广泛应用。由于拇指复杂的解剖学基础以及手指间显著的姿态变化，在非接触环境中包含拇指时无法获得满意的性能。为克服与拇指相关的障碍，本文设计了四种基于四指（排除拇指）的生物特征识别方法。本章提出了一种基于四指的生物特征识别方法。此外，通过消除不显著的特征来降低特征维度的显著特征选择至关重要。根据特征的判别效率分配权重，以强调关键特征。本文基于自适应前向选择与后向消除（FoBa）算法，采用了全局和局部两种特征选择策略。使用加权k近邻（wk-NN）和随机森林（RF）分类器评估识别性能。实验在Bosphorus手部数据库的300名受试者上使用选定的特征子集进行。通过基于排序的局部FoBa算法选出的25个特征子集，实现了98.67%的最佳识别准确率和4.6%的等错误率（EER）。