A New Fingerprint Enhancement Approach Using Image Fusion of Histogram Equalisation and Skeleton

Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. As a good start of work, fingerprint image enhancement has been focused on this study. It begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for experimental in this study. With the multi-type enhancement method, the fingerprint images became clearly visible

Level 3 Feature Based Fingerprint Identification

In this thesis, two novel schemes have been proposed: one scheme on dots and incipient ridges extraction and another scheme on matching using level 2 and level 3 features. Dots and incipient ridges are extracted by tracing valley. Starting points are found on the valley by analyzing the frequencies present in the fingerprint. Valleys are traced from the starting point using Fast Marching Method (FMM). An intensity based checking method is used for finding these feature points. Delaunay triangle has been constructed using level 2 feature. A novel algorithm of selecting compatible triangle pair from Delaunay triangle is proposed. A novel set of feature parameters are constructed by establishing spatial relation between minutiae and dots-and-incipient. Pore based matching has been performed using Robust Affine Iterative Closest Point algorithm. These extended features (dots, incipient ridges, and pores) are helpful for forensic experts. However, forensic experts deal with full-to-partial print matching of latent fingerprint. Hence, Full-to-partial fingerprint matching has been carried out. Partial print is constructed by cropping a window from a full fingerprint in two ways such as, non-overlapped cropping and random cropping. Form the experiment, it has been observed that random cropping based fingerprint has better accuracy than non-overlapped cropping. For performance evaluation of the proposed algorithm, two public databases have been used: NIST SD30 database and IIIT Delhi rural database. All images in SD30 are taken in constrained environment and images in IIIT database are taken in unconstrained environment. Feature level and score level fusion have been carried out for fusing different levels of feature. It has been observed that score level fusion shows better accuracy than feature level fusion

Automatic fingerprint classification scheme using template matching with new set of singular point-based features

Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. Thus, this thesis proposes a new classification technique based on template matching using fingerprint salient features as a matching tool. Basically, the methodology covers five main phases: enhancement, segmentation, orientation field estimation, singular point detection and classification. In the first phase, it begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. Then, at the beginning of the second phase, the image is partitioned into 16x16 pixels blocks - for each block, local threshold is calculated using its mean, variance and coherence. This threshold is then used to extract a foreground. Later, the foreground is enhanced using a newly developed filling-in-the-gap process. As for the third phase, a new mask called Epicycloid filter is applied on the foreground to create true-angle orientation fields. They are then grouped together to form four distinct homogenous regions using a region growing technique. In the fourth phase, the homogenous areas are first converted into character-based regions. Next, a set of rules is applied on them to extract singular points. Lastly, at the classification phase, basing on singular points’ occurrence and location along to a symmetric axis, a new set of fingerprint features is created. Subsequently, a set of five templates in which each one of them represents a specific true class is generated. Finally, classification is performed by calculating a similarity between the query fingerprint image and the template images using x2 distance measure. The performance of the current method is evaluated in terms of accuracy using all 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for development and testing of fingerprint classification systems. The experimental results are very encouraging with accuracy rate of 93.05% that markedly outpaced the renowned researchers’ latest works

FINGERPRINT RECOGNITION SYSTEM

This project is to design a fingerprint recognition system for security purposes. It will also explore and suggest some solution to the improvement to the existing fingerprint system. Security system that uses a pin code or access card can be easily misused or mishandled. A pin code can be cracked using some hacker software while an access card can easily be stolen or misplaced. Thus, these security methods are very vulnerable to hackers and criminals. Instead, a fingerprint is unique to every person and due to the fact that no two people have the same fingerprint pattern, it makes the fingerprint a very good resource in a security system. The aim of this project is to focus on the concept and methodology of the fingerprint recognition system. By grasping the concept and method of the fingerprint recognition flow, a prototype is developed that will compare an input fingerprint with its predefined template. The system should be able to compare and decide if the input fingerprint is the same as the predefined template. The output of the first stage is a preprocessing stage. There are two stages involved in preprocessing which is the image enhancement and image skeletonization. Fourier transfonn and histogram equalization is utilized to enhance the low quality image to a better image so that the feature extraction process will run smoothly. The second stage of the project is to define the orientation, ROI extraction and minutia extraction. The matching sequence and the angle orientation problem were resolved

A Biometric Approach to Prevent False Use of IDs

What is your username? What is your password? What is your PIN number? These are some of the commonly used key questions users need to answer accurately in order to verify their identity and gain access to systems and their own data. Passwords, Personal Identification Numbers (PINs) and ID cards are different means of tokens used to identify a person, but these can be forgotten, stolen or lost. Currently, University of Hertfordshire (UH) carries out identity checks by checking the photograph on an ID card during exams. Other processes such as attendance monitoring and door access control require tapping the ID card on a reader. These methods can cause issues such as unauthorised use of ID card on attendance system and door access system if ID card is found, lost or borrowed. During exams, this could lead to interruptions when carrying out manual checks. As the invigilator carries out checks whilst the student is writing an exam, it is often difficult to see the student’s face as they face down whilst writing the exam. They cannot be disturbed for the ID check process. Students are also required to sign a manual register as they walk into the exam room. This process is time consuming. A more robust approach to identification of individuals that can avoid the above mentioned limitations of the traditional means, is the use of biometrics. Fingerprint was the first biometric modality that has been used. In comparison to other biometric modalities such as signature and face recognition, fingerprint is highly unique, accepted and leads to a more accurate matching result. Considering these properties of fingerprint biometrics, it has been explored in the research study presented in this thesis to enhance the efficiency and the reliability of the University’s exam process. This thesis focuses on using fingerprint recognition technology in a novel approach to check identity for exams in a University environment. Identifying a user using fingerprints is not the only aim of this project. Convenience and user experience play vital roles in this project whilst improving speed and processes at UH

Fingerprint minutiae filtering based on multiscale directional information

Automatic identification of humans based on their fingerprints is still one of the most reliable identification methods in criminal and forensic applications, and is widely applied in civil applications as well. Most automatic systems available today use distinctive fingerprint features called minutiae for fingerprint comparison. Conventional feature extraction algorithm can produce a large number of spurious minutiae if fingerprint pattern contains large regions of broken ridges (often called creases). This can drastically reduce the recognition rate in automatic fingerprint identification systems. We can say that for performance of those systems it is more important not to extract spurious (false) minutia even though it means some genuine might be missing as well. In this paper multiscale directional information obtained from orientation field image is used to filter those spurious minutiae, resulting in multiple decrease of their number

Experimental Assessment on Latent Fingerprint Matching Using Affine Transformation

Abstract-In forensics latent fingerprint identification is critical importance to identifying suspects: latent fingerprints are invisible fingerprint impressions left by fingers on surfaces of objects. The proposed algorithm uses a robust alignment algorithm (mixture contour and Orientation based Descriptor) to align fingerprints and to get the similarity score between fingerprints by considering minutiae points and ridge orientation field information.The texture-based descriptors (local binary patterns and local phase quantization), address important issues related to the dissimilarity representation, such as the impact of the number of references used for verification and identification. However, the overlapped region shape similarity retrieved from minutiae spatial distribution information provides additional important criteria. After finding the overlapping region of a possible affine transform, we can measure to find the shape dissimilarity via the application of the shape context to all interior points.TheHybrid matching algorithm, is to prune outlier minutiae pairs, and secondly to provide more information to use in similarity evaluation