User identification system for inked fingerprint pattern based on central moments

The <span>use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.</span>

Cryptography based Internet Security ATM System using Fingerprint for securing PIN

Today, ATMs (automated teller machines) are used in a variety of ways, including: you can withdraw cash, transfer cash, check your balance, get an account summary, and more. The main component here is security, to maintain the security we prefer cryptography and biometrics. Cryptography is a state- of-the-art technique for increasing security. Using cryptography and biometrics together creates a bio cryptographic system. Since the private key is an important component of encryption, here one is the main task to secure the private [1] key. To overcome this issue, we use a biometric fingerprint image. However, biometrics cannot protect anyone’s fingerprint template because of the many suspected attacks. Therefore, we use a fuzzy vault system that combines cryptography [2] and biometrics [4] to ensure that only authorized users can use their passwords and PINs with the legal data. Keywords: Biometrics, Cryptography, Fuzzy vault system, IP security, Java Applets.

A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.