Fingerprint Recognition Projects

The investigation of two fingerprints images to know whether both fingerprint patterns belong to the same individual or not is called fingerprint recognition. For this purpose, at first, it extracts the unique patterns and then matches the extracted patterns with existing patterns for maximum similarities. If you are willing to do your research or final year fingerprint recognition projects, then this page will provide you with useful code development information!!!

Implementing Fingerprint Recognition Projects

Different Types of Fingerprint Matching Systems

Based on similarities, it determines whether the input and existing fingerprints are collected from the same individual or not. Actually, there are different types of matching systems as follows,

  • Pattern-based matching
  • Minutiae-based matching
  • Image-based matching
  • Correlation-based matching 

Fingerprint Recognition: Introduction 

Before the precise matching process, the other processes involved are image acquisition, image segmentation, pre-processing and feature extraction with interoperability.

As mentioned earlier, the main task of fingerprint recognition is to compare the unknown fingerprint against known fingerprints to inspect whether the fingerprints are the same. In general, the fingerprint recognition system is composed of two sub-systems as fingerprint verification and identification. In this, the fingerprint verification process is performed to verify a person’s legitimacy with personal credential information like user id/user name. Then, it collects the pre-stored fingerprint template using the person’s user id. After that, it matches the fingerprint against the newly collected template to authenticate a person’s identity. Commonly, it follows the working principle of the Automatic Fingerprint Authentication System (AFAS). 

When the fingerprint verification process is done manually, then it will be more tough, time-consuming, costly, and also not applicable for today’s fast world. So, automated fingerprint recognition projects are currently used in numerous real-world applications for security reasons. Here, we have given you actual elements in the fingerprint that are used for recognition. 

What exactly Fingerprint is it?

  • It is a colossal collection of furrows and ridges as basic pattern
    • Furrows – Lightest pattern found between the ridges
    • Ridges – Constantly flowing dark patterns
  • It also includes the tiny minutiae features which are found over ridges  

What is the purpose of fingerprint recognition?

As a matter of fact, the fingerprint recognition system analysis and relates the finger dermal ridges to identify or verify a person’s identity. Over other types of biometric techniques, the fingerprint recognition system is the first and foremost technique which effectively used in the current world. Also, it is improvising more in several aspects to meet the highest security and accuracy level among others.

Now we can see the general steps to develop a fingerprint recognition projects. These steps can be enhanced by utilizing advanced techniques. Let’s talk about how fingerprint images are recognized as follows,  

How is fingerprint recognized? 

  • Step 1 – Collect and segment the input fingerprint image
  • Step 2 – Prediction of orientation
  • Step 3 – Quality improvement of fingerprint image
  • Step 4 – Classify the fingerprint image by patterns
  • Step 5 – Perform ridge thinning over fingerprint image
  • Step 6 – Extract minutiae features from the fingerprint image
  • Step 7 – Match collected minutiae features with minutiae template for similarities

Next, we can see research challenges that are technically not solved effectively. Scholars choose any of these kinds of research issues as their research problem and perform in-depth exploration over a particular problem. Then, identify unique and effective solutions which have high performance. In this way, they prove their research ability to solve the particular research problem. For your information, here we have given you some important research issues in fingerprint recognition systems. Further, we also support you in developing suitable research solutions for your selected research issue. Beyond this list of issues, we also support you in other emerging research challenges from top research areas of fingerprint recognition. 

Major Challenges in Fingerprint Recognition 

  • Usability of various sensors
  • Low quality of input image collection
  • No fixed size for representing the fingerprint
  • High dimensionality in feature extraction in the input image
  • Lack of accuracy in minutiae matching and indexing
  • Insufficient representation for template security systems
  • Difficult to identify matching fingerprints over huge-scale database
  • No fault-tolerant algorithm against spurious/missing minutiae points
  • Mismatch fingerprints from the same finger due to some external impact like non-linear distortion, skin state, unbalanced pressure, global change, partial overlap, noise, etc.

Further, here we have given you some significant development tools, frameworks, and technologies for implementing fingerprint recognition projects. All these tools are truly best to develop your desired project with accurate results. Since we have developed so many projects in all these tools.

Particularly, these tools are well-sophisticated with libraries, toolboxes, modules, and packages to support all essential operations of the fingerprint. Our developers have sufficient knowledge in handpicking suitable libraries and functions based on project requirements and minimizing code length as well. Let’s see the tools that we are currently working on for our scholars. 

Development Tools for Fingerprint Recognition Projects 


  • Open-source software with many libraries and modules
  • Able to inspect random binaries lists with their dependencies. For instance: Swirl
  • Implement swirl application which can be traced over rocks cluster applications
  • Include the capability to trace stack for determining shred library 


  • Used to develop a fingerprint recognition system
  • Flexible to compare input image with stored fingerprints from database
  • Well-suited for all kinds of personal identification and authentication applications
  • Relative to other biometric systems, fingerprint identification earns more attraction
  • When the matches are found, it reveals a person’s identity or declare fake
  • Overall comparison process takes place based on extracted minute features
  • Then fingerprint recognition is embedded with integrated solutions  


  • Specifically offer separate Scilab Image Processing Toolbox
  • Extended version of Scilab which further comprises read and write images functions
  • Support nearly 100 image formats such as TIFF, JPEG, PNG, BMP, etc.
  • Able to perform image blurring, image filtering, image segmentation, image recognition, structure analysis, and edge detection 


  • Used to design and develop fingerprint projects using OpenCV interface
  • Initially, collect images from fingerprint sensors
  • Then, implement binarization over input data for denoising
  • Next, apply the image contrast technique for a better view of furrows and ridges
  • Utilize packages like SKimage, OpenCV2, NumPy, and other
  • Utilize Harris Corner detection for minutiae points

Due to the huge advantages of fingerprint recognition in security systems/applications, is widely recognized in several research fields as an embedded system. this makes scholars do their research projects in the finger recognition field. Each scholar is attempting to the ability to improve system performance in its way.

To make you unique from others, first of all, choose a unique research area and frame trending research ideas from the selected area. If you are a beginner in this field, then we support you with a list of current research areas. Once handpick your motivating area, then we also provide lists of project trends that are collected from your desired research area. For your reference, here we have listed some trending research perceptions of fingerprint recognition.  

Emerging Trends of Fingerprint Recognition 

  • Access Control in Physical System 
    • The identity of an employee is verified by means of fingerprints for business application access or employee attendance and time management
  • Mobile Identity Verification 
    • The identity of mobile users is verified through fingerprints for accessing mobile apps
  • Public Identity Verification Systems
    • The identity of public people is verified by fingerprint for government purposes like border security, voting, aadhar, etc.
  • Identity Protection 
    • Utilization of fingerprint recognition in the organization to detect and prevent fake identities and duplication
  • On-Boarding
    • The identity of employees and customers are verified in terms of fingerprints for organizations like fraud activities prevention

For your awareness, our development team has given you one sample project in the fingerprint recognition system. In this, there are three main modules as image acquisition module, image enhancement module, and matching module. Further, there are two levels of features where one is stored in the database and the other is extracted from the input. Let’s, in what way the identity of a particular individual is recognized using fingerprint images.

Implementation Steps of Fingerprint Recognition Project 

  • Step 1 – Collect input fingerprint image in acquisition module
  • Step 2 – Improve image by background prediction, noise removal, Gabor filtering, and skeleton analysis in the enhancement module.
  • Step 3 – Classify the level- features from enhanced image and store in fingerprints database
  • Step 4 – Detect and extract the segment from the original fingerprint image using crossing number
  • Step 5 – Extract the level-2 features by a neural network like multi-layer feedforward neural network
  • Step 6 – Pass level-2 feature vector as input to BOZORTH3 matcher for matching with stored features in matching module
  • Step 7 – Finally, recognize the identity of a person by a high matching score value

Last but not least, now we can see databases that are broadly utilized in fingerprint recognition projects. In each database, you can find a set of structured fingerprint images which are used to support fingerprint operational recognition and assessment.

In this, a person’s identity is basically apart from fingerprint data in databases for assessment. All these databases are open to all researchers who wish to do fingerprint recognition research work. And, these databases have raw fingerprint images which are collected from digitized fingerprints (collected from paper impressions), live-scan sensors, or cameras. Here, we also give you a list of datasets/databases that are apt for fingerprint recognition research thesis.

Datasets for Fingerprint Recognition 

  • FVC Database 
    • FVC2002 DB3 
      • Size: 300 x 564
      • Resolution:512 dpi
      • Sensor Type:Precise Biometrics – Capacitive Sensor “100 SC”
    • FVC2004 DB2
      • Size: 330 x 370
      • Resolution:520 dpi
      • Sensor Type:Digital Persona – Optical Sensor “U.are.U 4000”
    • FVC2000 DB1
      • Size: 280 x 280
      • Resolution:570 dpi
      • Sensor Type:KeyTronic – Low-cost Optical Sensor “Secure Desktop Scanner”
    • FVC2000 DB3
      • Size:453 x 474
      • Resolution:512 dpi
      • Sensor Type:Identicator Technology – Optical Sensor “DF-90”
    • FVC2004 DB1
      • Size:640 x 480
      • Resolution:520 dpi
      • Sensor Type:CrossMatch – Optical Sensor “V300”
    • FVC2004 DB3
      • Size:299 x 500
      • Resolution:500 dpi
      • Sensor Type:Atmel – Thermal Sweeping Sensor “FingerChip”
    • FVC2002 DB2
      • Size:300 x 565
      • Resolution:570 dpi
      • Sensor Type:Biometrika – Optical Sensor “FX2000”
    • FVC2002 DB1
      • Size:380 x 370
      • Resolution: 490 dpi
      • Sensor Type:Identix – Optical Sensor “TouchView II”
    • FVC2000 DB2
      • Size:254 x 366
      • Resolution:490 dpi
      • Sensor Type:ST Microelectronics – Low-cost Capacitive Sensor “TouchChip”
    • Sokoto Coventry Fingerprint Dataset (SOCOFing) 
      • Images – 6000+
      • Subjects – 600+ (African)
      • Attributes – finger, hand, gender (labels)
      • Alteration levels – z-cut, obliteration, central rotation
      • Purpose – Research and academic usage

On the whole, we are glad to say that we provide the fullest support in your fingerprint recognition projects starting from research to project execution. Beyond our list of research ideas, we also take into consideration of your research ideas. When you become a part of us by creating a bond with us, we assign you nearly 10+ experienced professionals for your project. 

All these professionals are allocated by our research, development, and thesis writing service,dissertation writing service. As well, they will take the whole in-charge of your project until your successful project submission. We ensure you that our suggested project topics are unique to create the best contributions in the selected project area of fingerprint recognition. Further, if you are curious to know other research details of fingerprint recognition projects then approach us.

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