A practice of detecting and verifying a single person’s fingerprint in an automatic way using pairs of fingerprint images is called Fingerprint recognition. Usually, there are different types of biometric solutions like iris, hand vein, finger vein, etc. Although there are more biometric techniques, fingerprint biometric system is always considered as the forever best solution due to their permanency, simplicity, reliability, adaptability (with other devices), automation, and cost-effectiveness. This page is prepared to showcase new research topics, issues, and toolboxes to develop Fingerprint Recognition using Matlab Project!!!

Now, we can see the general structure of the fingerprint. This structural analysis helps you to identify both the patterns and features of the fingerprint. Through fingerprint patterns, you cannot directly recognize the person’s identity. Since it is used to focus on search areas by eliminating other contextual details. By analyzing the features, one can deeply meet the person’s uniqueness to identify the person’s identity.

Our developers help you to detect both patterns and features of a fingerprint through advanced techniques at any level of low-quality images. Since we know the functionalities of each technique to acquire patterns and features in different levels of complexity.

Fingerprint Anatomy

• Generally, a fingerprint has a structural alignment of ridgelines and furrows
• As well, these ridges flow in a parallel way to form ridge patterns
• Further, there are tiny elements called minutiae features that are present in between the parallel lines.

Moreover, we have also given you the basic steps involved in fingerprint recognition. Actually, the general operations in many fingerprint recognition using Matlab projects are minutiae feature extraction, quality enhancement, image segmentation, feature extraction (in 3 levels), and feature matching.

All these operations are aimed to recognize the real identity of a person for authentication. If the fingerprint is recognized to be original in the matching process, then the person has a real identity else the person is a fake user who illegally attempts to access the system. At that time, it denies the accessibility of the fake person.

Major Steps in Fingerprint Recognition Using Matlab Project

• Minutiae Extraction from Fingerprint Image
  • In fact, minutiae features in fingerprint are identified and determined in terms of ridge bifurcation or ridge endings
  • By the by, it is expressed as (x, y) coordinates, angle of θ between horizontal axis, minutia tangent along with minutiae type (bifurcation / endings)
  • In short, the fingerprint image is classified into frequency image, orientation image, and fingerprint foreground to form ridge pattern. Then, collect minutiae from ridge pattern
• Various Patterns of Minutiae Features
  • Arch
  • Plain Arch
  • Tented
  • Whorl
  • Right Loop
  • Left Loop
• Conventional Methods for Minutiae Extraction
  • Binarization/Enhancement
  • Transform input image into binary image
• Thinning
  • Minimize ridge thickness into one pixel for the purpose of thinning binary image
• Detection
  • Scan the thinned binary image to identify minutiae
• Quality Enhancement in Fingerprint Image
  • Basically, image quality acts as influential factor for the efficiency of feature extraction and matching techniques
  • The main intention of the enhancement method is to increase the image quality
• Fingerprint Image Segmentation
  • Mainly, segmentation is intended to detach a particular fingerprint region
  • In other words, one can say isolating foreground from a background of the input image
  • Here, foreground represents oriented and striped pattern and background represent a uniform pattern
• Feature Extraction from Fingerprint Image
  • In truth, feature extraction is the core phase in fingerprint classification
  • Also, there are different hierarchy levels of features
  • Level 1 Features (Patterns)
    • It represents the whole pattern (i.e., shape/structure) of unknown fingerprint
      • For instance: arch, whorl, and loop (left and right)
    • It does not directly find a person’s unique identification instead it narrows down the search
  • Level 2 Features (Minutiae Points)
    • It represents particular friction ridge paths and their minutiae characteristics and deviations
      • For instance: islands, scars, ridge endings, flexion creases, lakes, bifurcations, and incipient ridges
  • Level 3 Features (Ridge Shape and Pores)
    • It represents core regions of fingerprint
      • For instance: edge details, pores, scars, ridge units, etc.
    • It requires sensors at high resolution (∼1000dpi)
• Feature Matching between Fingerprint Images
  • Find the matching minutiae features between two fingerprints patterns
  • Match extracted features against the stored template for fingerprint recognition

How to measure the quality of fingerprint images?

The de facto standard used to quantify the quality of fingerprints is NIST Fingerprint Image Quality (NFIQ). The open-source NFIQ (1.0) will allocate a value to fingerprint which ranges between 1 to 5. As well, it is inversely proportional to the image quality. Overall, NFIQ is a measurement of quality to predict and evaluate the performance of an automated fingerprint recognition system. In the following, we have listed the maximum value and minimum value of fingerprint image quality.

• Value 5 – low accuracy, bad quality, and major errors
• Value 1 – high accuracy, high quality, and minor errors

Next, we can see the recent research issues in fingerprint recognition systems.

Latest Issues of Fingerprint Recognition

• Quality-based Issues
  • Highly Wet or Dry Fingerprints
  • Scars in Ridgelines (old people / manual workers)
• Degradation-based Issues
  • No Space between Parallel Ridges
  • No Continuity in Ridge Lines
  • Bruises, Cuts, Wounds, Creases on Finger
• Image-based Issues
  • Complex Context Info
  • Lack of Contrast / Brightness
  • Finger Rotation and Distance
  • Natural Lighting
• Other Significant Issues
  • Double-Identity Fingerprint
  • Forged Fingerprint
  • Latent Fingerprint
  • Modified Fingerprint

With an intention to enhance fingerprint images, contextual (background) filters are largely used in fingerprint recognition using Matlab project. Based on the local context, the characteristics of utilized contextual filtering will vary. Enhancing the image quality will increase the performance of fingerprint identification, authentication, classification, etc.

Overall, it gives you the best results in developing real-time fingerprint recognition systems. Here, we have given you significant research topics of fingerprint recognition based on some key operations.

Latest Research Topics in Fingerprint Recognition

• Fingerprint Image Classification
  • Image Categorization based on Delta Type and Core
• Fingerprint Image Identification and Verification
  • Fingerprint Matching Algorithm / System Design
  • Minutiae-based Fingerprint Similarities Detection
• Correlation Methods for Fingerprint Authentication
  • Feature Extraction using Wavelet Domain
  • Gabor Filter for Fingerprint Verification
  • Correlation Detection over Paired Fingerprint Images
• Fingerprint Image Compression
  • WSQ Standardization for Image Compression

In addition, we have given you the fundamental toolboxes introduced particularly for fingerprint recognition using Matlab projects. Even though different tools are available for fingerprint recognition, Matlab acquires a special position among other development tools. Since it is well-equipped with necessary toolboxes and functions to implement all sorts of complex operations in fingerprint recognition.

For instance: the image acquisition toolbox helps you to collect input fingerprint images from different sources (10 fingers) at high speed. For your reference, here we have listed out a few important Matlab toolboxes where each one unique set of functionalities.

Matlab Toolboxes for Fingerprint Recognition

• Computer Vision Toolbox
• Image Processing Toolbox
• Deep Learning Toolbox
• Image Acquisition Toolbox

More than these above-specified toolboxes, we also support you in other vital toolboxes. Furthermore, we have also given you some key functions used for fingerprint recognition systems. Similar to toolboxes, each function has a unique set of objectives to implement a particular task in fingerprint recognition. In this, we have mentioned image smoothing and image normalization tasks as samples. Also, it includes required input and output parameters.

Functions in Matlab for Fingerprint Recognition

Smoothen the orientation image

• function oimg = smoothen_orientation_image (ori_img)

Smoothens the frequency image via diffusion process

• function fimg = smoothen_frequency_image (frq_img, RLOW,RHIGH,diff_cycles)

Normalise the input image between 0 to 1 at required variance and mean value

• nor = normalise (in_img, req_mean, req_var)
• Arguments

• req_var – Required image’s variance
• in_img – Gray-scale input image
• req_mean – Required image’s mean value

Likewise, we keenly achieve an expected result on every task of fingerprint recognition. Since our developers are effective to perform numerical analysis to deal with any sort of complex function.

Next, we can see in what way the input fingerprint image is getting normalized and how the ridge area is segmented from the normalized image. At first, this function divides the input image into multiple blocks (where size = blksze x blksze) and determines the standard deviation for each area. When the determined standard deviation is greater than the threshold value, then the particular part is treated as a significant portion of the fingerprint.

Before implementing this function, make sure that your normalized input image has a unit standard deviation and 0 means. This helps to achieve a threshold value that is relatable to unit standard deviation. Let’s see the input parameters and output parameters of function which are used for fingerprint image normalization and ridge region segmentation.

Normalises fingerprint image and segments ridge region

[norm_img, mask, mask_indi] = ridgesegment(img, blksze, thresh_val)

Input Parameters

• thresh_val – When a block is ridge area, set value threshold of standard deviation

• For example: 0.1 – 0.2 values
• img – Fingerprint image that going to be segmented
• blksze – Size of block on determined standard deviation
  • For example: 16

Output Parameters

• mask – Mask that indicated ridge areas in an image (1 – ridge area and 0 – non-ridge area)
• mask_ind – Indices of locations in vector which present inside the mask
• norm_img – Normalized image where ridge areas have unit standard deviation and zero mean

Determine the local orientation of ridges in a fingerprint normalized input image

[Orientimg,reliability]=ridgeorientation(Img,Gradientsigma,Blocksigma,Orientsmoothsigma)

Input Parameters

• Gradientsigma – Gaussian sigma for determining image gradients
• Img – Normalised input image
• Orientsmoothsigma – Gaussian sigma for smoothing overall orientation vector field
• Blocksigma – Gaussian-weighted sigma for computing total gradient moments

Output Parameters

• reliability – Compute the reliability of orientation which ranges from 0 to 1. 0.5 is treated as reliable value
• Orientimg – Orientation image which +ve clockwise radians as orientation values

`Besides, we have also given you the distance function for matching the extracted feature. This function has the objective to find Euclidean distance between two sets of vectors matrices for similarity identification.

Fingerprint matching can be performed in different ways like pattern matching, minutiae feature matching, correlation matching, etc. Here, we have highlighted the feature matching based on distance function in detail. In this way, we also support you in other matching techniques of fingerprint recognition using Matlab.

Feature Matching using Distance function

function D = DIST2(X, C)

Aim

DIST2 is used to compute squared distance among 2 sets of points

Explanation

D = DIST2(X, C). It takes 2 vector matrices as input and compute squared Euclidean distance among those vector matrices. By the by, both vector matrices should have same column dimension. When X has M number of rows and N number of columns (i.e., X = M x N) and C has L number of rows and N number of columns (i.e., C = L x N), output will be M number of rows and L number of columns (i.e., res = M x L). Further, (i, j) entry is the squared distance from ith row of X to jth row of C.

So far, we have fully discussed the important research issues, research topics Matlab toolboxes, and functions. Now, we can see how the best project is developed in fingerprint recognition using Matlab tool. In this, we have handpicked the double identity fingerprint recognition system as an example.

Specifically, we have used a deep convolutional neural network as patch learning technique as the proposed solution. Further, we have performed ridge similarity detection, optimal cutline estimation, etc. for identifying double identity fingerprint recognition (i.e., fake identity of individual). In the following, we have given you sample project for fingerprint recognition.

Best Fingerprint Recognition using Matlab Project

• In advanced biometric solution, automated person identity recognition is possible using universally accepted fingerprint sensors
• In specific, a double-identity fingerprint is considered to be a fake fingerprint
• Since, the double-identity fingerprint is developed from two fingerprints alignment for high ridge similarity and cutline estimation
• Further, fake fingerprint comprises criminal features and enrol his innocence accomplice over automated machine-readable document
• As well, it is utilized to cross over the border gates through claiming accomplice ID
• In order to solve this problem, use deep CNN-based patch learning technique
• By training the network, this technique determines the cutline to detect and realize patterns surrounded over the region of joint fingerprint
• In recent days, this technique become a new fingerprint recognition technique
• Moreover, we have produced new database which 500+ double identity fingerprints
• To the end, this technique proved that Deep Learning related methods are capable to predict cutline at an equal error rate
• So, it is efficient to compare with other handcrafted features for the purpose of double identity fingerprint recognition
• In short, form the fingerprint ridge alignment from two input fingerprint images. Then, estimate the optimal cutline. Next, from cutline estimation, compute weighted combination for double identity fingerprint detection

Likewise, we help you to develop your handpicked project in an efficient way to reach your desired project results. Further, we have itemized some present research trends in the fingerprint recognition system. We have a resource team from all parts of the world to collect up-to-date research developments information. They are intelligent to analyze and find every possibility of research in the fingerprint recognition area.

All our provided research ideas are sure to prove your research passion in your desired research area. Beyond this list of ideas, we also have numerous research ideas to support you in all applicable research aspects of fingerprint recognition.