Identification and verification of human fingerprint similarities is known as fingerprint recognition. Compare to other biometric solutions, it is the best one to use in many current real-world authentication applications and systems. It is a computerized system to collect fingerprint images as input from the fingerprint sensor. In fingerprint recognition using matlab we processes, extracts, and matches the patterns/features of a fingerprint with already stored fingerprint(s) either in one-to-one or one-to-many aspects.
The main reason to choose fingerprint recognition over other biometric is the simplicity of image acquisition and global acceptance. By the by, there are different kinds of sources in this biometric over each person.
What is meant by Fingerprint Recognition?
As mentioned earlier, fingerprint recognition currently gained more publicity among the research community and industrial community by different aspects such as consistency, uniqueness, permanency, etc. Also, it is a widely used automated biometric solution because of its improved computing abilities.
One important thing in the fingerprint recognition system is the ease of image acquisition from different sources (i.e., 10 fingers) and their convenient usage by immigration and law enforcement.
Now, we can see the fundamental processes incorporated in fingerprint recognition systems. There are four stages in fingerprint recognition such as image acquisition, preprocessing, feature extraction, and feature matching.
The main aim of the fingerprint recognition system is to compare fingerprint images to verify whether the impression belongs to the same individual or not. These four stages are sure to reach the objective of fingerprint recognition. Further, it also includes other stages/operations based on the handpicked project.
On this page, we are going to give you the complete research scope of fingerprint recognition using Matlab with its research issues, techniques, functions, toolboxes, programming languages, etc.
What are the processes in fingerprint recognition?
- Image Acquisition
- Acquire fingerprint image from any source
- Preprocessing
- Perform image enhancement and create segmentation mask
- Implement binarization and thinning methods
- Feature Extraction
- Identify the minutiae features and filter out false minutiae
- Matching
- Load the database and register images for computing matching score
For a fingerprint recognition system, fingerprint pattern/type is the most essential one to narrow down the search area. Finger pattern is identified to recognize the type of finger that the person holds. Also, it is a preliminary step to be performed in a fingerprint recognition system.
In recent research, it is found that finger patterns are inherited element which passes over from generation to generation. So, the person of the same family also can acquire the same pattern. By the by, the loop is the most common pattern and the arch is the least common pattern in the fingerprint recognition field.
Types of Fingers for Fingerprint identification
- S-type
- Tent
- Whorl
- Balloon
- Arch
- Left Loop
- Eddy Loop
- Right Loop
Problem Statement for Fingerprint Recognition
Although fingerprint recognition is extensively implemented in several real-world scenarios until now accurate fingerprint matching is a challenging task. This is because of complicated distortion/noise in two fingerprints of the same finger. Consequently, the chance of incorrect authentication for a reliable person is high. So, this system requires the best fingerprint matching techniques.
Majorly, the matching techniques completely depend on minutiae features which are more than enough for Automatic Fingerprint Recognition System (AFRS). Further, there are different aspects to increase the AFRS system. Some of the main aspects are given in the following bulletins.
Requirements for Improving AFRS Accuracy
- Pre-processing Algorithms
- Feature Extraction Algorithms
- Post‐processing Algorithms
- Image Enhancement Algorithms
- Quality of Image
Next, we can see the primary parameters that influence the Fingerprint Recognition using Matlab. Parameters are used to analyze the working nature of the algorithms/techniques. So, you need best-fitting parameters for your project that definitely increase the performance of algorithms/techniques. There are several performance parameters available in fingerprint recognition systems. To increase your knowledge on parameters, we have given you only the top 3 parameters which create an impact on fingerprint recognition system performance.
Major Parameters that affect the performance of fingerprint recognition
- Number of Pixels
- It represents the total number of pixels in an input image of fingerprint
- Resolution
- It represents the total number of pixels for each inch (dpi)
- For instance: 500dpi which is least resolution for FBI-compliant scanners
- Area
- It represents the rectangular area that is covered by fingerprint scanner for sensing
- It also expressed in inch2
Further, we have given you some important research issues in developing a fingerprint recognition system. All these issues are lacking ineffective techniques to enhance fingerprint recognition system performance. Therefore, these research issues are looking for effective research solutions.
What are the challenging issues of Fingerprint Recognition?
- Low Image Quality
- Quality of image greatly create an impact on minutiae features extraction of over fingerprint
- When the image quality is good, it is efficient to extract the strong feature set
- When the image quality is low, it majorly extracts spurious features and lacks genuine features
- So, a new fingerprint recognition system is required to include an image enhancement module to improve the image quality in noisy input images
- Lack of Accurate Performances
- One of the primary research challenges that deals with error rates
- Require to reduce False Reject Rate (FRR) and False Accept Rate (FAR)
- Insufficient Automated Classification Technique
- Recent improvements in computing abilities increase the developments of Automated Fingerprint Authentication Systems (AFIS)
- In this, it is required to improve the classification methods
Moreover, our developers are also good to analyze other emerging research challenges and providing suitable research solutions in fingerprint recognition using matlab. If there are complex challenges, then we design a new algorithm to tackle complexity in an effective manner.
Generally, fingerprint features are majorly extracted from different features of friction ridges. And, it is further classified into three categories as ridge features, macroscopic ridge patterns, and minutiae features. All these patterns and features are varying from person to person eve for identical twins.
So, all these are considered as a unique pattern that has characteristics of uniqueness, permanency, consistency, reliability, etc. Below, we have specified some key features of fingerprint identification.
What are the important features of fingerprint recognition?
- Ridge Contour and Pores Features
- For instance: Growing Pores, Width, Ridges, Shape, etc.
- Flow Patterns of Macroscopic Ridge
- For instance: Delta points and Core
- Minutiae Features
- For instance: Ridge Bifurcation and Ridge endings
As a matter of fact, our developers have handled all these features with advanced feature extraction techniques. We are capable to guide you on the right path of feature identification and extraction based on your project goal. Since we have sufficient practice in working with different mathematical and matrices operations.
In addition, we have given you some important fingerprint recognition techniques/methods which are related to feature/pattern identification and extraction. All these techniques are efficient to analyze the hidden minutiae features which give accurate differentiation in a fingerprint pattern.
Methods for Fingerprint Recognition
- Ridge Feature-oriented
- Correlation-oriented
- Co-efficient of Correlation
- Minutiae-oriented
- Gray-scale
- Binarization
Further, we also support you in other developing techniques and algorithms. We ensure you that we accurately extract the essential features of fingerprints even in extremely low-quality images. Since we have sophisticated technical resources like development tools and technologies to handle complex scenarios of fingerprint recognition.
Next, we can see some important functions that are identified in many fingerprint recognition projects. We are best to utilize not only the fingerprint recognition using matlab techniques but also their functions. For illustration purposes, here we have given you a function that is used to enhance the quality of the fingerprint image along with its input parameters and output parameters. In addition to image enhancement, further, we have also given you a feature extraction function. Since image enhancement and feature extraction are the most essential operations in fingerprint recognition systems.
Functions Available for Fingerprint Recognition using Matlab
Enhances the fingerprint image
[en_img, ori_mg, bw_img, fr_img, enh_img] = fft_enhance_cubs(img, BLKSZ)
function [en_img, fr_img, enh_img, c_img, ori_img, bw_img,]= fft_enhance_cubs(img, BLKSZ)
- img
- It notifies the input image of fingerprint which should be in DOUBLE type [IN]
- en_img
- It specifies energy image which represents the block ridgeness for segmentation [OUT]
- ori_img
- It addresses the image in block orientation [OUT]
- bw_img
- It denotes the image in angular bandwidth which is adaptive to singular points [OUT]
- fr_img
- It signifies the image in block frequency (indicates ridge spacing) [OUT]
- enh_img
- It addresses the improvement of image [OUT]
Already, we have seen the feature extraction methods in the above section. Now, we can see the feature extraction functions that are commonly found in fingerprint recognition systems. In this, we have mentioned function along with passing input arguments and return value. Further, we have also given you the method to identify and extract the minutiae feature points in the fingerprint image.
EXTRACTING FEATURE FROM A FINGERPRINT IMAGE
- Function extr_fea [ ret ] = ext_finger( img, display_flag);
Argument
- display_flag – Display Flag
- img – Input Fingerprint Image
- Return Value
- ret – Minutiae Feature
Find False Minutae Points in fingerprint image
Distance_Threshold = 12; /* Threshold value for distance
Distance = DistEuclidian(Centroid_Bif,Centroid_Term);
False_Minutae = Distance < Distance_Threshold;
[p,q]=find(False_Minutae);
Centroid_Bif(p,:)=[];
Centroid_Term(q,:)=[];
In recent days, many image processing tools are introduced for fingerprint recognition. Relatively, MATLAB is a sophisticated tool to develop all kinds of fingerprint recognition systems regardless of complexity. It is open-source software that can be easy to embed with any computing device. Also, Matlab enables you to reuse the code which is developed in another programming language. Further, it also supports the C program which is developer-friendly to write an error-free program for hardware, inter-responsive websites, etc.
Matlab Interface Support for Fingerprint Recognition
- Python Libraries
- OpenCV Library
- TensorFlow Library
- ONNX Support (for DL Techniques)
On using MATLAB Engine APIs, one can able to implement Matlab code from other programming environ. These APIs are capable to implement Matlab code in your selected programming language. Also, it does not require starting the Matlab desktop session while project execution.
For your information, here we have given you some important APIs used in the Matlab engine to support other programming environ. Further, COM components support languages like visual basic, .Net, visual C#, etc. Let’s see other programming languages that are supported in Matlab.
Calling other programming Languages in Matlab
- Java
- C++ / C
- Python
- Fortran
To acquire live images and video, it is required to link with cameras by using some hardware support packages. Moreover, the live information can be collected from DCAM cameras, GigE Vision cameras, and frame cameras.
In addition, MATLAB allows you to work with different image formation and standard data. As well, you can also access your required information through pre-defined applications and functions. Further, it enables ImageDatastore to store and manage imported large image datasets. The following cameras are supported in Image Acquisition Toolbox from Matlab projects. Also, these cameras are well-suited for collecting real-time images and video.
Import Support (Video and Image) by Direct Camera Access
- Matrix Vision Camera
- Lumenera Camera
- Point Grey Camera
- PixeLINK Camera
Last, of all, we are glad to say that we give the best research services in all the research areas of the fingerprint recognition system. We believe that the above-specified fingerprint recognition using matlab information is more useful to confidently begin your research project. Further, if you have any doubts about this article or you like to know more information then have contact with our team.
