Natural resources in tropical countries

xxxi, 549 hlm.: ill.; 23 cm

The petroleum resources of Indonesia.

The petroleum resources of Indonesia.

A secure digital camera based fingerprint verification system

Contemporary fingerprint system uses solid flat sensor which requires contact of the finger on a platen surface. This often results in several problems such as image deformation, durability weakening in the sensor, latent fingerprint issues which can lead to forgery and hygienic problems. On the other hand, biometric characteristics cannot be changed; therefore, the loss of privacy is permanent if they are ever compromised. Coupled with template protection mechanism, a touch-less fingerprint verification system is further provoked. In this issue, a secure end-to-end touch-less fingerprint verification system is presented. The fingerprint image captured with a digital camera is first pre-processed via the proposed pre-processing algorithm to reduce the problems appear in the image. Then, Multiple Random Projections-Support Vector Machine (MRP-SVM) is proposed to secure fingerprint template while improving system performance

Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network

Image-based handwritten signature verification is important in most of the financial transactions when a hard copy of signature is needed. Considering the lack of dynamic information from static signature images, we proposed a working framework through hybrid methods of discrete Radon transform (DRT), principal component analysis (PCA) and probabilistic neural network (PNN). The proposed framework aims to distinguish forgeries from genuine signatures based on the image level. Extensive experiments are conducted on our own independent signature database, and a public signature database – MYCT. Equal error rates (EER) of 1.51%, 3.23% and 13.07% are reported, respectively, for random, casual and skilled forgeries of our own database. When working on the MYCT signature database, our proposed approach manages to achieve an EER of 9.87% with 10 training samples