27-02-2013, 10:40 AM
Touch-less Fingerprint Analysis — A Review and Comparison
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Abstract
Touch-less fingerprint recognition system is a reliable alternative to conventional touch-based fingerprint recognition system. Touch-less system is different from conventional system in the sense that they make use of digital camera to acquire the fingerprint image where as conventional system uses live-acquisition techniques. The conventional fingerprint systems are simple but they suffer from various problems such as hygienic, maintenance and latent fingerprints. In this paper we present a review of touch-less fingerprint recognition systems that use digital camera. We present some challenging problems that occur while developing the touch-less system. These problems are low contrast between the ridge and the valley pattern on fingerprint image, non-uniform lighting, motion blurriness and defocus, due to less depth of field of digital camera. The touch-less fingerprint recognition system can be divided into three main modules: preprocessing, feature extraction and matching. Preprocessing is an important step prior to fingerprint feature extraction and matching.
Introduction
Fingerprint recognition system is a biometric system that uses fingerprint as biometric input to this system. A fingerprint consists of patterns of ridges and valleys on the surface of a fingertip. Each individual has fingerprint which is different from the other. Actually this biometric system is a computer vision system which performs following functions: Image acquisition, Pre-processing, Feature Extraction, High-level processing or verification or matching. Basically, Fingerprint recognition system is an identification system that can be an Automated Fingerprint Identification System (AFIS) or a Non-automated Fingerprint Recognition System. Earlier, we used to take fingerprints using “ink techniques” in which black ink is spread on fingertip and it is pressed against a paper card, it is also called as “off-line fingerprint acquisition technique”[1]. This technique is used in the law enforcement to acquire criminal’s fingerprints. Nowadays, live-scan acquisition technique is used in civil and criminal AFIS (Automated Fingerprint Identification system), that make use of sensors like optical, solid-state to acquire fingerprints.
Disadvantages of Touch Based Sensors
While using the touch-based sensor interface, the
fingertip needs to be placed over the interface so that a
proper fingerprint image can be taken. But the touchbased
sensors have several problems like the problem of
contamination which occurs because of placing the
fingertip over the same interface which is already used
by other. This produces a low quality fingerprint image.
Another problem is due to contact pressure, which
creates physical distortions which are usually non-linear
in arbitrary direction and strength. Moreover, the
distortion occurs globally, while its deformation
parameters could be different locally in a single
fingerprint image [2]. Fig 1 shows the fingerprint image
of one fingertip but with different minutiae because of
physical pressure [2].
Pre-Processing
Preprocessing is an important step prior to fingerprint feature extraction and matching. As the fingerprint images are captured using digital camera which had certain challenging problems as stated earlier so, these fingerprints require more preprocessing over them. Pre-processing is divided into four blocks.
Normalization
Fingerprint Segmentation
Fingerprint Enhancement by STFT analysis
Core Point Detection
Normalization
Normalization is the first preprocessing operation. It can be done in two ways for two different purposes. In the first way [3], normalization is done so as to minimize the non-uniform lighting problem. It can be done by changing the dynamic range of the pixel intensity values. It calculates the mean and variance of an image and thus reduces the difference of the illumination.
Fingerprint Enhancement using STFT Analysis
The fingerprint image may be thought of as a system of oriented texture with the local ridge orientation and ridge frequency varying slowly throughout the image [6]. Due to this non-stationary nature of the image, traditional Fourier analysis is not adequate to analyze the image completely. So it is required to resolve the properties of the image both in space and also in frequency [6]. The steps in this proposed analysis [6] are:
The image is first divided into overlapping windows during STFT analysis. The overlapping window is used to preserve the ridge continuity and removes ’block’ effects common with other block processing image operations. The image is assumed stationary within this small window and can be modeled approximately as a surface wave. Probabilistic estimates of the ridge frequency, ridge orientation and energy map are obtained after the Fourier spectrum of this small region is analyzed
Core point Detection
To differentiate the entries of fingerprint images singular points, SPs are used. SPs are points that can be consistently detected in a fingerprint image and can be used as a registration point. Typically there are two types of singular points: core point and delta point. In this paper we only proposed the core point detection method. Fingerprint’s core point can be defined as the point of maximum curvature in the fingerprint image.
A fingerprint can have two structures, the global and the local structure. In the global structure the overall pattern of the ridges and valleys are considered where as in local structure the detailed pattern around a minutiae point is considered. A minutiae point is a position in the fingerprint where a ridge is suddenly broken or two ridges are merged.
Feature Extraction
Most fingerprint identification methods use minutiae as the fingerprint features [7]. The steps involved in minutiae extraction are smoothing, local ridge orientation estimation, ridge extraction, and thinning and minutiae detection. But for a small scale system, it is not efficient to process all the steps. So, we proposed Gabor filter-based feature extractor [8] because its frequency and orientation representation are similar to those of human visual system. Also Gabor filter helps in smoothing out noise and preserving true ridge valley structures.
Conclusion
In this paper, a review of touch-less fingerprint recognition system, which can be an automated or a biometric digital camera based system is presented. We also presented number of comparisons between touch-less systems and the conventional fingerprint recognition systems. Further, this paper presented a modeled system that comprised of preprocessing, feature extraction and matching. Preprocessing is further subdivided into normalization, segmentation, enhancement and core point detection. The feature extraction might be based on minutiae extraction or image based method that is Gabor filter in which feature vectors are extracted. Moreover we have presented an effective verification technique that employs the SVM classifier and compares it with three distance measures.