Biometric Security Using Finger Print Recognition

Our goal is to implement finger print recognition on the PXA27x DVK platform in view of increasing popularity of biometric security for digital handheld devices. To this end we are using Siemens ID Mouse finger print capture device mentioned in [5] that scans the finger print and gives out a high quality image while maintaining sub-pixel geometric accuracy. This image will then be stored and processed to extract the critical pixel information (minutiae). Subsequent scans on any finger print other than that of the authorized user(s) will be disallowed. A secure process will be then forked based on the authorization. The image recognition algorithm has to be highly precise and also efficient enough to enable accurate instantaneous access. 1. Project Background The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. It is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies. The three basic patterns of fingerprint ridges are the arch, loop, and whorl. An arch is a pattern where the ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger. The loop is a pattern where the ridges enter from one side of a finger, form a curve, and tend to exit from the same side they enter. In the whorl pattern, ridges form circularly around a central point on the finger. 1.1 Fingerprint sensors A fingerprint sensor details of which can found in [4] captures the digital image of a fingerprint pattern called live scan. This live scan is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. The commonly used fingerprint sensor technologies are optical, ultrasonic and capacitive. Siemens ID Mouse falls under the capacitive category of fingerprint sensors. Capacitance sensors utilize the principles associated with capacitance in order to form fingerprint images. The two equations used in this type of imaging are: where, C is the capacitance in farads, Q is the charge in coulombs, V is the potential in volts, ε0 is the permittivity of free space, measured in farad per metre, εr is the dielectric constant of the insulator used, A is the area of each plane electrode, measured in square metres, d is the separation between the electrodes, measured in metres. As shown in Fig. 1 texture of the fingerprint formed by cured alternation of the ridges and valleys has to be transformed into the digital image. For this purpose, the distance between sensor surface and finger ridges or valleys is transformed to the discrete information, and from this information grey level representation of discrete levels is given on the output. The underlying principles are discussed in greater detail in [7].