Comparison of two fingerprints is to check whether the digital impressions are collected from the same individual or not is said to be as fingerprint recognition. In fact, it uses several image processing techniques to find an individual’s identity for authentication. Are you searching for trustable research and code development information about fingerprint recognition using python? Then, this article is sure to satisfy your requirements!!!
Basically, the fingerprint has some unique features like skin elasticity, skin surface properties, texture, BP, optical properties, temperature, perspiration, electrical conductivity, skin color, pulsation, odor, etc. Similarly, it includes recognition characteristics as fingerprint patterns and minutiae features.
In general, unique features are extracted from fingerprints as ground truth and stored in the database for future purposes. When the input fingerprint image processing, it extracts the minutiae features from the input image and compares them with a stored template for similarities identification. For feature extraction and matching processes, various standard techniques are available which capable to deal with binary images. Further, here we have given you some important features that should be focused on in fingerprint recognition systems.
Important aspects of fingerprint recognition
- Finger Positioning
- Fingerprint Class / Type
- The density of Ridge Line
- Quality of Fingerprint
- Skin Misrepresentation
In fingerprint recognition, the generation of datasets is the simplest process for examining the effect of given parameters like image quality? Here, the ground truth of the image should be unique and achievable even the image quality is not sufficient in the outcome. Recently, many research solutions are developed to solve the quality issue.
One more important open issue in fingerprint identification and authentication systems is the fake detection technique. When it is known that this technique is followed in fingerprint recognition systems, a fake finger attack has the possibility to fool that particular system.
Now, we can see the latest research challenges involved in fingerprint recognition. Although the fingerprint recognition system provides you more benefits in securing personal / organization information, it has some technical issues while executing on real-world scenarios. Here, we have given you limitations regarding image quality and image collection.
Limitations of Fingerprint Recognition
- Image quality over real-time fingerprints
- Existence of small pores and cores in the ridges
- Discontinuity in ridges and also have various contact with the sensor surface
- Physical accidents on fingertip like small scratches/cuts
- Collection of fingerprint images from large-scale databases
- Tedious for users to submit different acquisition sessions at different time/date
- Sometimes cause problems in privacy legislation to safeguard Personal information
- Luxurious utilizing time and money
All these above-specified limitations are expected to be solved by effective research solutions. Since it is widely recognized as a major research challenge in recent fingerprint recognition. One more major research challenge is large data handling. In order to avoid the utilization of a huge-scale dataset, the skin distortion model is efficient to implement over randomly produced real fingerprints of the same “synthetic finger”.
Similarly, our development team is intelligent to provide you best solutions for all other research challenges of the fingerprint recognition system. To know suitable research solutions for these and other research limitations, interact with our team.
Next, we can see about the recent research areas that have more research opportunities and challenges to acquire the best fingerprint recognition project ideas. We ensure you that all our research ideas are unique and new to this world. Further, we also support you in other major research areas to support you in research implementation of fingerprint recognition using python projects.
Research Areas in Fingerprint Recognition
- Indexing of fingerprint
- Fingerprint authentication
- ISO template similarities
- Template fingerprint identification
- Orientation extraction over fingerprint
For any kind of fingerprint recognition project, performance is a most important one. Since performance is highly required to prove that your research work is more efficient than previously conducted researches. In order to evaluate the performance, there exist several performance assessment metrics. All these metrics are intended to examine the efficiency of proposed solutions to reach targeted performance.
Similarly, some factors affect the performance of fingerprint recognition. These factors are essential to be concentrated on while proposing and developing research fingerprint recognition using python projects. We are here to provide your smart techniques to deal with all these performance influential factors.
What are factors influence the performance of fingerprint recognition?
- Minutiae location
- Foreground map (a binary image which specifies external shape of fingerprint)
- Local orientation image
- Minutiae directions
- Local frequency image
- Binary image (noise-free master fingerprint)
There are many key operations present in different stages of fingerprint recognition. As well, some of the key operations are image acquisition, image pre-processing, image enhancement, pattern/feature identification, feature extraction, image matching, etc.
For illustration purposes, here we have taken feature extraction as an example. In this, we have mentioned important extraction techniques that are largely utilized in fingerprint recognition using python. From our experience, we found that the following techniques give projected research even in low-quality images.
Common Feature Extraction Methods for Fingerprint Recognition
- DNN-based Features
- In recent times, the deep neural networks is evidently proved to be effective for the extraction of fixed length descriptions on rolled fingerprints
- Minutiae-based Features
- It matches the all possible minutiae pairs among two different templates that have minutiae features
- Correlation-based Features
- In various alignments, determine the pixel correlation and overlaid fingerprint patches
- Mainly, used in limited regions and low-resolution sensors
In the above list, we have specified fundamental extraction techniques for fingerprint recognition systems. Here, we have specified advanced feature extraction techniques that are capable of tolerant external environmental disturbance.
New Feature Extraction Methods for Fingerprint Recognition
- Slit-oriented Techniques
- Gradient-oriented Techniques
- Projection-oriented Techniques
- Time / Frequency-domain Analysis Techniques
- And other emerging techniques
What are the techniques used for fingerprint recognition?
Now, we can see the basic functions associated with fingerprint recognition using python. In general, python is enriched in libraries and modules to support you in every function of fingerprint identification and verification. At first, it collects the database and splits it into training data and testing data. Then, perform efficient pre-processing and feature extraction (singularity) techniques over both training and testing data.
Next, perform the required training and testing process for fingerprint classification. At last, display the acquired fingerprint classification outcome. Below, we have given you the general operations involved in fingerprint recognition using python.
- Preprocessing
- Main intention is to enhance the quality of fingerprint images by low-pass filtering, contrast enhancement, etc.
- Particularly, need more attention in low-quality image
- Majorly not dependent on orientation-based background filtering
- Functionally required for orientation estimation and feature extraction processes
- Once orientation is extracted, it can regain to the original image for future processing
- Local Analysis
- On utilizing image data like color intensity, pixel, etc. estimate every orientation θi,j from a local window that centrally positioned in [xi, yj ]
- Further to determine orientation, implement various local analysis techniques
- Global analysis
- In the local analysis and smoothing techniques, orientations are difficult to find in extremely noisy areas
- So, global analysis techniques are introduced to compute the global orientation
- Use information from good quality areas of the image to improve the low-quality areas
- Due to constrained fingerprint orientation variation, good quality orientation can frame restriction in problematic areas
- To implement global analysis techniques, first, determine the initial orientation from local analysis
- Postprocessing
- Implement standardization over local orientation with objectives to maximize image quality and minimize the noise in low-quality areas
Next, we can see about the important python libraries which broadly preferred by developers for fingerprint recognition. Relative to other development technologies, python acquires significant recognition among the research community for its code development. Since python is an object-oriented programming language that is sophisticated with several python libraries.
All these libraries are enriched with functions that support all complex operations of image processing. Also, these libraries are best to minimize the code length of fingerprint recognition projects without compromising accurate results. Since each library is comprised of more functions to execute a set of image processing operations.
Imported Python Libraries for Fingerprint Recognition
import os
import cv2
import numpy
import sys
from skimage.morphology import thin, skeletonize
import matplotlib.pyplot
from enhancing import image_enhance
The main intention of biometric solutions is to provide security and access control for users’ personal information. If the attempting user is not the right person, then access to the system/application will be immediately denied. In comparison with other biometric solutions, the fingerprint is broadly recognized in many security apps and control mechanisms. Here, we have given you important steps to identify and authenticate fingerprints by matching process.
Steps Involved in Fingerprint Recognition using Python Project
- Step 1 – Get the input image and preprocess the collected image
- Step 2 – Segment the image block which has a central position of the fingerprint
- Step 3 – Implement fuzzy rule logics over feature points of the fingerprint
- Step 4 – Generate fuzzy feature point image and joint image block
- Step 5 – Implement CNN for training recognition model
- Step 6 – Match the stored fingerprint with the trained fingerprint
- Step 7 – Display the recognition outcome over matching
For every fingerprint recognition project, the database is the most important thing to achieve the expected result. Since fingerprint recognition is a data-intensive system that highly depends on the input dataset/database of the project. Here, we have itemized list of databases in NIST DB and FVC database classifications. All these databases are available on the internet for free download. Further, there are also some commercial databases that need to pay for the download.
Databases for Fingerprint Recognition
NIST DB Database
- NIST DB 27
- Dataset – Latent fingerprint images
- Categories – Bad, Ugly, and Good
- NIST DT 10 / 4 / 14 / 9
- Database – 1000+ images (by rolled ink fingerprints)
- NIST DT 24
- Database – 100+ live video sequences
- Categories – 10 individuals
- Purpose – Inspecting the impact of plastic distortion and finger rotation
FVC Database
- FVC2002
- Each Database Size – Testing: 100 x 6 and Training: 10 x 6
- Complexity – Database 1 to 4 (low)
- Total Databases – 4
- FVC2006
- Each Database Size – Testing: 140 x 10 and Training: 10 x 10
- Complexity – Database 1 (high), Database 4 and 2 (low) and Database 3 (moderate)
- Total Databases – 4
- FVC2000
- Each Database Size – Testing: 100 x 6 and Training: 10 x 6
- Complexity – Database 4, 1 and 2 (low) and Database 3 (high)
- Total Databases – 4
- FVC2004
- Each Database Size – Testing: 100 x 6 and Training: 10 x 6
- Complexity – Database 1 to 4 (moderate)
- Total Databases – 4
Once you provide your code development responsibility to us, we help you to choose the appropriate dataset/database for your project based on your project requirements. Further, if you want to know other non-commercial databases for fingerprint recognition projects then make a bond with us.
Next, we can see the performance metrics of the fingerprint recognition system. There are several performance metrics to assess the system efficiency and behavior in execution. For fingerprint recognition using python, precision, recall, and accuracy are the most important metrics used to assess the classification process in fingerprint recognition. Further, there are other performance metrics utilized to improve and validate your system performance.
Performance Analysis of Fingerprint Recognition
- Equal-Error Rate
- When FNMR and FMR are the same, it measures the error rate at the specific threshold value
- Precision
- It measures the fraction of obtained documents which are related to findings
- Accuracy
- It computes the relativeness of quantitative measurement to the real quantity
- ZeroFMR
- When FMR exists, it calculates the least FNMR
- ZeroFNMR
- When FNMR does not exist, it determines the least FMR
- Recall
- It measures the fraction of obtained information in the document which is related to query (successful data retrieval)
In addition, we have also given you some important python libraries that are well-suited for current fingerprint recognition systems. You can recognize these libraries in the majority of fingerprint identification and authentication projects using python. Since, all these libraries provide necessary mathematical, logical, and statistical functions.
So, all the below-specified libraries are efficient to develop desired fingerprint recognition using python programming in a short period of time with 100% good quality results. Further, we also suggest other inclusive libraries for your selected project based on your project objectives.
Python Libraries for Fingerprint Recognition
- Scikit-image
- The python-enabled open-source module which operates with Numpy arrays
- Introduced for implementing utilities and algorithms for industrial and research applications/systems
- Developed by volunteers which is peer-reviewed and high-quality
- NumPy
- It is an important basic library to perform array related operations in python
- It stores the image numpy array format which comprises data points as pixels
- Helps to perform operations like masking, indexing, and slicing to modify pixel values
- Able to load the image using skimage and view using Matplotlib
- SciPy
- It is similar to Numpy which at as core library
- Mainly intended for image processing and manipulation
- Include sub-module called scipy.ndimage (in SciPy v1.1.0) for working with n-dimensional Numpy arrays
- Support binary morphology, object measurements, filtering (non-linear and linear), and B-spline interpolation
- OpenCV
- It is expanded as Open Source Computer Vision Library
- Include python APIs that written in C++ /C programming languages
- Proposed majorly for machine vision and image processing applications
- Developers choice to implement computationally-intensive CV programs
- PIL/Pillow
- It is expanded as Python Imaging Library
- Open-source Python library for image processing
- Support manipulation of various image format
- Simple to install and adaptive to embed in many operating systems
- Include a collection of intuitive convolution kernels
- Enable to work on all image processing tasks such as color-space conversion, filtering, point operations, etc.
To the end, we are here to join with you to guide you on the hurdle-free path of your research journey. Overall, we support both research scholars and final year students for the best fingerprint recognition system. Also, we support you to develop a fingerprint recognition using python and also tools like Matlab; similarly, we support you not only on fingerprint recognition but also on iris recognition, facial emotion recognition, hand vein recognition, finger vein recognition, etc.
