Finger Print Recognition and Image Enhancement Using Matlab
Instrumentation Engineering by the BPUT, ODISHA during the academic year 2011 – 2012. It is certified that all corrections/suggestions indicated for internal assessment have been incorporated in the report. The project report has been approved as it satisfies the academic requirements in respect of project work prescribed for the above degree. Project Incharge Head of Department “He who receives a benefit should never forget it” Behind every success there lies a master hand which creates concentration, dedication, encouragement, enthusiasm and ecstasy.
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Low quality fingerprint image, distortion, the partial image problems, large fingerprint databases are all major areas of research needed to improve the accuracy of current systems. 1. 2 What is a fingerprint? Fingerprints are the patterns formed on the epidermis of the fingertip. The fingerprints are of three types: arch, loop and whorl. The fingerprint is composed of ridges and valleys. The interleaved pattern of ridges and valleys are the most evident structural characteristic of a fingerprint. There are three main fingerprint features a) Global Ridge Pattern ) Local Ridge Detail [pic] Fig 1. 1 Fingerprint Image c) Intra Ridge Detail Global ridge detail: There are two types of ridge flows: the pseudo-parallel ridge flows and high-curvature ridge flows which are located around the core point and/or delta point(s). This representation relies on the ridge structure, global landmarks and ridge pattern characteristics. The commonly used global fingerprint features are: i)Singular points – They are discontinuities in the orientation field. There are two types of singular points- core and delta.
A core is the uppermost of a curving ridge, and a delta point is the point where three ridge flows meet. They are used for fingerprint registration and classification. ii )Ridge orientation map – They are local direction of the ridge-valley structure. It is helpful in classification, image enhancement, feature verification and filtering. iii)Ridge frequency map – They are the reciprocal of the ridge distance in the direction perpendicular to local ridge orientation. It is used for filtering of fingerprint images. Local Ridge Detail:- This is the most widely used and studied fingerprint representation.
Local ridge details are the discontinuities of local ridge structure referred to as minutiae. They are used by forensic experts to match two fingerprints. There are about 150 different types of minutiae. Among these minutiae types, ridge ending and ridge bifurcation are the most commonly used as all the other types of minutiae are combinations of ridge endings and ridge bifurcations. (a)(b) (c) (d) (e)(f) Fig 1. 2 Types of minutiae The minutiae are relatively stable and robust to contrast, image resolutions, and global distortion when compared to other representations.
Although most of the automatic fingerprint recognition systems are designed to use minutiae as their fingerprint representations, the location information and the direction of a minutia point alone are not sufficient for achieving high performance. Minutiae-derived secondary features are used as the relative distance and radial angle are invariant with respect to the rotation and translation of the fingerprint. Intra Ridge Detail On every ridge of the finger epidermis, there are many tiny sweat pores and other permanent details. Pores are distinctive in terms of their number, position, and shape.
However, extracting pores is feasible only in high-resolution fingerprint images and with very high image quality. Thus the cost is very high. Therefore, this kind of representation is not adopted by currentautomatic fingerprint identification systems (AFIS). 1. 3 Fingerprint recognition Fingerprint recognition is one of the popular biometric techniques. It refers to the automated method of verifying a match between two fingerprint images. It is mainly used in the identification of a person and in criminal investigations. It is formed by the ridge pattern of the finger.
Discontinuities in the ridge pattern are used for identification. These discontinuities are known as minutiae. For minutiae extraction type, orientation and location of minutiae are extracted. Two features of minutiae are used for identification: termination and bifurcation. [pic] (a) Ridge ending (b) Bifurcation Fig 1. 3 Types of local ridge features [pic] Figure 1. 4(a) two important minutia (b) Other minutiae features Finger print recognition includes two sub-domains: one is fingerprint verification and the other is fingerprint identification.
In addition, different from the manual approach for fingerprint recognition by experts, the fingerprint recognition here is referred as AFRS (Automatic Fingerprint Recognition System), which is program-based. [pic] Figure 1. 5 Verification vs. Identification However, in all fingerprint recognition problems, either verification(one to one matching) or identification(one to many matching), the underlining principles of well-defined representation of a fingerprint and matching remains the same. The advantages of fingerprint recognition system are (a) They are highly universal as majority of the population have legible fingerprints. b) They are very reliable as no two people (even twins) have same fingerprint. (c) Fingerprints are formed in the fetal stage and remain structurally unchanged throughout life. (d) It is one of the most accurate forms of biometrics available. (e) Fingerprint acquisition is non intrusive and hence is a good option . 1. 4 Approach There are three approaches for fingerprint recognition. They are image based approach, texture based approach and minutiae based approach. In image based matching, the image itself is used as the template. It requires only low resolution images.
Matching is done by optical correlation and is extremely fast. It is based on the global features of a whole fingerprint image. However it requires accurate alignment of the fingerprint samples and is not favorable for changes in scale, orientation and position. The second is the texture based approach. It uses texture information for matching and performs well with poor quality prints. However like image based matching it requires accurate alignment of the two prints and not invariant to translation, orientation and non-linear distortion. Minutiae-based approach is the last approach.
Here the ridge features called minutiae are extracted and stored in a template for matching. It is invariant to translation, rotation and scale changes. It is however error prone in low quality images. The minutiae based approach is applied. Usually before minutiae extraction, image preprocessing is performed. In our project we have focused mainly on the preprocessing and extraction stage. Fingerprint enhancements techniques are used to reduce the noise and improve the clarity of ridges against valleys. The image preprocessing consists of the following stages.
They are field orientation, ridge frequency estimation, image segmentation and image enhancement thinning. It is followed by a minutiae extraction algorithm which extracts the main minutiae features required for matching of two samples. Image Acquisition Image acquisition is the first step in the approach. It is very important as the quality of the fingerprint image must be good and free from any noise. A good fingerprint image is desirable for better performance of the fingerprint algorithms. Based on the mode of acquisition, a fingerprint image may be classified as off-line or live-scan.
An off-line image is typically obtained by smearing ink on the fingertip and creating an inked impression of the fingertip on paper. A live-scan image, on the other hand, is acquired by sensing the tip of the finger directly, using a sensor that is capable of digitizing the fingerprint on contact. Live-scan is done using sensors. There are three basic types of sensors used. They are:- Optical sensors Optical sensors use arrays of photodiode or phototransistor detectors to convert the energy in light incident on the detector into electrical charge.
The sensor package usually includes a light-emitting-diode (LED) to illuminate the finger. There are two detector types used by optical sensors, charge-coupled-devices (CCD) and CMOS based optical imagers. CCD detectors are sensitive to low light levels and are capable of making excellent grayscale pictures. However, CCD fabrication is relatively expensive and neither low-light sensitivity or grayscale imaging are required for fingerprint recognition. CMOS optical imagers are manufactured in quantity and can be made with some of the image processing steps built into the chip resulting in a lower cost.
Optical sensors for fingerprints may be affected by a number of real world factors such as stray light and surface contamination, possibly even a fingerprint impression left by a prior user. Common contaminates that deteriorate image quality include oil and dirt, scratches on the sensor surface, and condensation or ice. Some suppliers have tried to sidestep the contamination problem by directly taking a 3D image from the surface of a finger. 3D imaging technology is more hygienic but introduces a whole new set of problems and was not included in this study.
Impostor prints are more of a problem for optical sensors than it is for other detectors because it is relatively easy to present the scanner with a convincing picture of a fingerprint. Suppliers have come up with several techniques to validate a live finger. For example optical sensors can be enhanced and made more resistant to deception with Electro-Optical imaging. This works by placing a voltage across a light-emitting polymer film. When a finger is presented, the ridges provide a ground to the polymer surface creating a small current that generating light.
The fingerprint valleys remain dark so a high contrast image is produced. The polymer is directly coupled to an optical detector. Figure 2. 1 Fingerprint scanning using optical sensor Optical sensors capture a digital image of the fingerprint. The light reflected from the fingerpasses through a phosphor layer to an array of pixels which captures a visual image of the fingerprint. Ultrasonic sensors use very high frequency sound waves to penetrate the epidermal layer of skin. The sound waves are generated using piezoelectric transducers. The reflected wave measurements can be used to form an image of the fingerprint.