| 摘要: | 最近，利用生物特徵作為身分認證技術的安全控制系統變得很重 要，過去幾年已有許多討論生物特徵認證技術的文章發表，這些文 章所描述的方法在使用上均受到某些限制，例如，擷取手掌影像時 需要一個固定手掌位置及方向的裝置，確保手掌在影像中之位置及 方向不變，以便可以從不同影像中相同之手掌區域萃取出生物特 徵；利用掌印影像當作認證的目標物；擷取影像時需要提供適當的、 充足的光線。這些限制造成使用者的不方便，也限制了生物特徵認 證技術的實用性。本論文發展出一個新方法，可以避免或消除上述 的限制。我們的方法可完全消除使用固定裝置(docking device)的 限制，也可克服使用掌印的限制，光線條件的限制也可避免。 論文中利用手掌的生物特徵提出兩個生物特徵認證技術，一個 是利用主掌紋為特徵，另一個技術是利用掌背血管作為認證的特 徵。這兩個方法的重要特性是不需事先知道認證目標物的相關知 識，所有參數均可自動設定；此外，上述的三種使用上的限制也可 避免。最後我們也提出實驗結果驗證方法之效果。 Recently, personal verification based on biometric features gradually becomes an important and highly demand technique for security access systems. During the past, numerous literatures discussing biometric verification using palm features have been reported. However, they are all constrained by some limitations, such as the utilization of docking devices to constrain the palm position while acquiring palmprint images, the applying of inked palmprint images as the objects, and the requirements of adequate lighting conditions, etc. These limitations hinder the conveniences of users and the practicalities of verification methods. In this dissertation, novel methods are devised and developed to alleviate or remove theses limitations. In our work, the limitation imposed by the docking devices is removed completely. Furthermore, the inconveniences introduced by the applying of inked palmprint images as objects are avoided. Finally, the restrictions of lighting conditions are also avoided. In this dissertation, we propose two approaches of biometric verification based on the palm features, which are principal palmprints and vein-patterns of palm-dorsa, respectively. The crucial characteristics of the proposed methods are that no prior knowledge about the objects is necessary, the parameters can be set automatically, and the limitations as mentioned above can be alleviated. In the palmprint verification approach, scanner is adopted as the input device for capturing palmprint images with the advantages of no palm inking and no requirement of docking device. Two finger-webs are automatically selected as the datum points to define the Region of Interest (ROI) in the palmprint images. Next, hierarchical decomposition is employed to extract the principal palmprint features inside the ROI, which includes direction and multiresolution decompositions. The former extracts principal palmprint features from each ROI. The latter processes the images with principal palmprint features to extract the dominant points from the images at each resolution. Finally, normalized correlation function is utilized to evaluate the similarity between two palmprint images. Experiments were conducted on a wide variety of palmprint images and the results are satisfactory with acceptable accuracy rate (FRR: 0.75% and FAR: 0.56%). The results reveal that our proposed approach is feasible and effective in palmprint verification without the needs of docking devices or palm inking. In the vein-patterns verification approach, an infrared (IR) camera is adopted as the input device to capture the thermal images of palm-dorsa. Likewise, two of the finger-webs are automatically selected as the datum points to define the Region of Interest (ROI) on the thermal images. Within each ROI, feature points of the vein-patterns (FPVPs) are extracted by modifying the basic tool of watershed transformation based on the properties of thermal images. According to the heat conduction law (the Fourier law), multiple features can be extracted from each FPVP for verification. Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. The former is integrated by an inter-to-intra personal variation ratio and the latter is integrated by a stack filter. We also introduce a logical and reasonable method to select a trained threshold for verification. The proposed approach can achieve an acceptable accuracy rate (FRR: 2.3% and FAR: 2.3%). The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification. |
