The method of vicinity minutiae decomposition with higher level graphs for fingerprint verification

The subject matter of the paper is the development of fingerprint local structures based on the new method of the minutia vicinity decomposition (MVD) for the solution to the task of fingerprint verification. It is an essential task because it is produced attempts to introduce biometric technology in different areas of social and state life: criminology, access control system, mobile device applications, banking. The goal is to develop real number vectors that can respond to criteria for biometric template protection schemes such as irreversibility with the corresponding accuracy of equal error rate (EER). The problem to be solved is the problem of accuracy in the case of verification because there are false minutiae, disappearing of truth minutiae and there are also linear and angular deformations. The method is the new method of MVD that used the level of graphs with many a point from 7 to 3. This scheme of decomposition is shown in this paper; such a variant of decomposition is never used in science articles. The following results were obtained: description of a new method for fingerprint verification. The new metric for creating vectors of real numbers were suggested – a minimal path for points in the graphs. Also, the algorithm for finding out minimal paths for points was proposed in the graphs because the classic algorithm has a problem in some cases with many points being 6. These problems are crossing and excluding arcs are in the path. The way of sorting out such problems was suggested and examples are given for several points are 20. Results of false rejection rate (FRR), false acceptance rate (FAR), EER are shown in the paper. In this paper, the level of EER is 33 % with full search. 78400 false and 1400 true tests were conducted. The method does not use such metrics as distances and angles, which are used in the classical method of MVD and will be used in future papers. This result is shown for total coincidences of real number, not a similarity that it is used at verifications. It is a good result in this case because the result from the method index-of-max is 40 %.

Multifactor Authentication Car Tracking System Using Fingerprint Verification

The design of an intelligent system used to detect and locate vehicle theft has become a viable and sustainable tool in the security system globally. Multifactor authentication car tracking system works in a way that if an unauthorized person tries to steal the vehicle, the user and user’s relatives and a registered police station will be notified with the GPS location. The fingerprint records are stored in the memory of the system. When the fingerprint matches with the stored ones, the microcontroller triggers and powers the circuit. The GPS module gets the location information from satellites in the form of location coordinates. The GSM module sends a short message service immediately to notify the owner in case of any theft action. The fingerprint test-scan results of approximately 100 percent competency level demonstrate that this technology has an enormous potential to enhance effective security and tracking technology in vehicles, objects and humans. Keywords: Tracking system, GPS, Fingerprint, Module. PACS: Electronic, 07.50.EK, 84.30.-r.

Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning

The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. The challenge is to develop an efficient, robust and automatic system for cross-sensor matching. This paper proposes a new cross-matching system (SiameseFinger) using the Siamese network that takes the features extracted using the Gabor-HoG descriptor. The proposed Siamese network is trained using adversarial learning. The SiameseFinger was evaluated on two benchmark public datasets FingerPass and MOLF. The results of the experiments presented in this paper indicate that SiameseFinger achieves a comparable performance with that of the state-of-the-art methods.

AN ADAPTIVE ALGORITHM BASED AUTHENTICATION SYSTEM FOR REAL-TIME BIOMETRIC TIME-ATTENDANCE SYSTEM

Biometric features are physical and biological characteristics that are unique to a person and can be used to accomplish authentication based on the particular modality. The main purpose of implementation of an Adaptive auto-correction technique for biometric time-attendance system is to improve the matching rate of fingerprint verification under the condition when fingerprint patterns vary due to environmental parameter like temperature. An Adaptive auto-correction technique is proposed which auto-corrects the reference fingerprint template at the time of genuine user rejection. The proposed technique is implemented on commercially available biometric device which uses Innovatrics, a standard commercially available extractor and matcher. Evaluation is carried out on 250 fingerprint templates of 10-users captured at varying temperature from 250C to 00C. The experimental analysis will be carried out to improve the recognition rate.

Improved Facial Recognition for Attendance Systems

Attendance is an important part of educational life. The attendance methodology which exists currently is either totally manual or requires some human assistance. In traditional methods, the teacher manually marks the attendance of each student on paper. There are many flaws in this method. As the process is manual there is a chance of error in the marking of attendance. Similarly, marking of false attendance by the students is also possible. Moreover, the attendance is then required to be updated in the online database of the institute through which it is accessible to the students. Also, after every month a report of all the students is to be generated. This overall process is cumbersome and time consuming. There also exist some automated systems for attendance like fingerprint verification and RFID but they also require some human support. Here, the automated attendance system using facial recognition plays an important role which requires no human intervention and is fully automatic. Face recognition has always remained a major focus of research because of its non-invasive nature and because it is people's primary method of person identification.