Human Identification Using Selected Features From Finger Geometric Profiles
提出一种非约束环境下的手指生物识别系统，通过图像归一化和特征选择算法，从手指几何轮廓中提取9或12个判别特征，在Bosphorus手部数据库上使用随机森林分类器达到96.56%和95.92%的识别准确率。
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 rightand left-hand images of 638 subjects, respectively. An equal error rate of 0.078 is obtained for both types of the hand images.