In hand vein recognition system, it uses a hand vein scanner to collect hand images for identifying the vein pattern of a person. Since it is used for authenticating a person’s individuality or identity. Therefore, it is called a hand vein-based biometric recognition system. This biometric system analyses unique patterns of a person’s human parts. However, it has threats of forgery, external injuries, duplication, etc. Through this page, you can track new research and code development information on hand vein recognition python projects!!!
Now, the biometric recognition system is improved to identify a patient’s vein pattern which is unique and permanent throughout life. Moreover, veins are not get affected by skin disease, nature, or other external injuries. Since it is safely placed under the skin surface of the human body. And also, it can be verified by a living person only. Further, the scanners used for hand vein recognition are non-intrusive, easy-to-use, intuitive, and stigma-free.
Types of Hand Vein Recognition
Although veins are available all over the body parts, hand vein patterns are unique for every individual even for identical twins. Therefore, it is accurate to check an alive person’s identity while important application accessibility. In general, there are 2 different types of vein pattern recognition systems. As well, they are finger vein recognition and palm vein recognition. Both of these biometric authentications use near-infrared light.
- Pattern Recognition over Finger Vein
- Scan the finger of a person which deals with a smaller surface area
- Relative to palm vein, it is a compact recognition method
- A person needs to place finger accurately on small scan region which lacks user-friendliness
- Smaller surface area may lead to lower reference points which are difficult to recognize patterns
- Although it is difficult to process, a vein pattern recognition system will provide a high level of security than other biometric recognition systems
- Pattern Recognition over Palm Vein
- Similar to finger vein, palm vein also has unique patterns
- In the case of palm, it has a larger surface area which increases reference points
- So, vein patterns are easy to identify and verify than finger vein recognition system
- While scanning palm veins, the patterns of veins are encoded in the scanner itself
- As a result, it assures extreme security and accuracy biometric authentication systems
Even though hand vein recognition is more advantageous than finger vein recognition and another biometric recognition system, it still has constraints in development. Here, we have listed some important hand vein recognition systems. Our developers are proficient enough to deal with complex research limitations/issues in hand vein recognition. Since we have successfully solved several issues by proposing many smart solutions effectively. Further, if you want to know solving mechanisms for below specified limitation of hand vein recognition then approach us.
What are the limitations of hand vein recognition?
- Greater EER
- Lower precision
- Able to assess only one dataset
- Need more efficient testing techniques
- Perform testing process over the small dataset
- High variation while unclear veins
Generally, a hand vein recognition system undergoes operations such as image acquisition, feature extraction, pattern matching, and evaluation. For illustration purposes, here we have taken feature extraction as an example. Here, the feature extraction is nothing but the abstraction of unique patterns from the input vein image. Specifically, it identifies the vein’s anatomical structure such as width variability, solidness, directionality, smoothness, and pattern continuity. By the by, the techniques used for feature extraction of veins are classified into 3 primary categories. As well, they are given as follows for your references.
Feature Extraction Methods for Hand Vein Recognition Python
- Local Binary Patterns
- Image Transformations
- Dimensionality Reduction
In addition, we have given you effective algorithms to enhance feature extraction. These algorithms are sure to achieve predictable results in hand vein recognition. Moreover, we also have practice handling other significant algorithms of other operations.
When the handpicked research problem is more challenging to solve, then we suggest either hybrid techniques or our algorithm by highlighting the required parameters. Anyhow, we crack the research problems in our way of strategies despite the complexity.
List of Feature Extraction Algorithms
- Gabor Filters
- Maximum Curvature
- Repeated Line Tracking
- Local Moments Extraction
- Principal Curvature
- Hybrid Techniques (Binarization, Normalized Local Gradient, and Principal Curvature)
- Pattern Anatomical Structure Extraction
Further, we have also mentioned some techniques/algorithms for hand vein recognition. Beyond this list of methods, we give our fullest support on machine learning, deep learning, and reinforcement learning algorithms. We ensure you that our developers are intelligent to adapt all these algorithms and methods in hand vein recognition python projects. Moreover, the algorithms may vary based on your proposed project objectives. As mentioned earlier, we also propose new algorithms to enhance the performance of the developed system.
Hand Vein Recognition Methods
- City block
- Distance Measurement
To design, develop and assess the finger-vein recognition system, three fundamental building blocks are required. As well, they are listed below for your reference.
Once you create a bond with us to develop your project, our developers will take whole duties to select appropriate software like a tool, programming language, library, framework, and modules.
Then, we suggest you choose fitting hardware like development platforms and technologies. Besides, we also assist you to handpick suitable datasets to perform train and test operations over hand vein recognition.
Requirements for Hand Vein Recognition
- Software or Library to perform biometric authentication
- Hardware to scan the image and implement technologies
- Include enough dataset (set of images)
Next, we can see in what way the hand vein recognition system is executed using python. For illustration purposes, here we have given you two different scenarios such as CNN-based Transfer Learning and CNN-based Feature Extraction. In this, we have specified steps to train hand vein images and select early layers and final layers features from input images. Likewise, we also provide flawless guidance on other important operations and techniques of hand vein recognition python.
How does hand vein recognition is implemented?
- CNN-based Transfer Learning
- Load pre-trained network
- The first layers collect low-level features like colors, edges, blob, etc.
- Replace final layers where new layers collect features over the certain data set
- Train the network
- CNN-based Feature Extraction
- Load pretrained network using 1000s classes and 1 million images
- The first layers collect low-level features like colors, edges, blob, etc.
- Extract important features
- Implement ECOC and KNN with ECOC and SVM
- Estimate and assess network precision
Moreover, we have also given you the different ways to enhance the efficiency of the proposed hand vein recognition project. From our long-lasting experience, we are adept to work with any sort of complex biometric recognition system. So, we know the possible technical issues in the practical execution of hand vein recognition systems. Consequently, we know the important aspects range from data collection to system evaluation. This makes the proposed system of pattern recognition and image analysis more efficient than others.
How to improve the recognition performance?
- Use Industrial Camera for NIR Image Acquisition
- Utilize NIR-based improved industrial camera
- In comparison with modified visible light cameras, NIR improve industrial camera has a high quantum efficiency
- Consequently, it achieves high image quality and contrast
- Efficient Vein Recognition using Biometric Fusion
- Utilize lighting of 2 different wavelengths to collect hand and vein images from scanners
- Flexible to implement Biometric Fusion method at the sensor level
- Therefore, it is simple to recognize patterns and improve performance
- Rapid Vein Image Acquisition
- Rapid image collection because of efficient acquisition technique and automatic brightness control
- Relatively, hand vein image acquisition is much faster than a finger vein acquisition
- Hand-vein – <1 second
- Finger-vein – 2 to 4 seconds
For the development of a hand vein recognition system, there are numerous software libraries and frameworks. As mentioned earlier, we guarantee you to provide the finest suggestion in selecting both software and hardware-based on your project objectives. Relatively, the following software is efficient to enable developers to feel user-friendliness while implementing a hand vein recognition system. By the by, we are also ready to serve in other software libraries and frameworks.
Available software frameworks/libraries
In recent times, the growth of different hand vein recognition python projects over real-world applications is uncontrollable. Since python is open-source software that is supplemented with a greater number of libraries and modules. All these libraries and modules are significantly dealing with different operations of hand vein recognition systems. To make you aware of core libraries that are essential to building hand vein recognition python projects. More than these libraries, we also recommend other Python libraries based on your project requirements.
Python Libraries for Hand Vein Recognition
- Opencv -python
- pyfingerprint 1.5
- TensorFlow Core v2.6.0
- xbob.fingervein 1.0.0
For illustration purposes, now we can see about PalmveinRecLib, pyfingerprint 1.5, and xbob.fingervein 1.0.0 from the above python libraries list. Likewise, other libraries also have individual python functionalities to perform a specific set of operations in a hand vein recognition system using python.
- Go to PyPI webpage and download the recently released version of PalmveinRecLib
- pyfingerprint 1.5
- Use ZhianTec ZFM-60 / -100 / -20 / -70 fingerprint sensors for PyFingerprint library
- Enable to work with Raspberry Pi and add-on Linux machines
- Supportive Models – FPM10A, R306, R302, R551, R305, R303, R307and etc.
- Installation Command – pip install pyfingerprint
- xbob.fingervein 1.0.0
- Intended to do a comparison among finger vein recognition
- Comprises scripts for implementing different finger vein recognition
- Further, use various types of finger vein image databases
- For Help type below command
- $ bin/fingerveinverify.py –help
- Give python-based interface for performing image pre-processing, feature extraction, and pattern matching over ginger vein database
- Includes various conventional and advanced finger vein recognition techniques
- Wide Line Detector (WLD)
- Maximum Curvature (MC)
- Repeated Line Tracking (RLT)
- Moreover, developing finger vein authentication systems is treated as the main hardware topology
- Installation Command – pip install xbob.fingervein==1.0.0
To the continuation of software libraries, now we can see about the different datasets that are widely in python-based hand vein projects. In this, the datasets are differentiated by object samples, object subjects, number of loaded images, image format, image resolution, and other specifications. By comparing all these specification factors, one should choose the best-fitting dataset for your proposed project. When you are new to this field, we help you to optimum one among short-listed datasets. In addition, we also have a greater number of commercial and non-commercial hand vein datasets.
Datasets for Hand Vein
- Vera Palm Vein
- Bosphorus Hand Vein
- Hand Vein 950 nm
- Protect HandVein
- Hand Vein 850 nm
- Tecnocampus Hand Image
Next, we can see various python-based hand vein identification project ideas. As a matter of fact, all these project ideas are selected from excellent research areas. Further, if you are curious to know the latest research areas or other significant project ideas then contact our team. By the by, we are not limited to the below-specified project ideas. Beyond this, we have numerous creative projects from every aspect of the hand vein recognition project. To the great extent, we also support your recommended project topics.
Top project ideas on hand vein recognition
- Smartphone-based Vein Biometric Recognition System
- Local Features Matching for Hand-Dorsa Vein Recognition
- Dorsal, Wrist and Palmar Biometric Verification using Multimodal Vein Database
- Deep CNN-based Pattern Verification on Minutiae Weighting Aggregation for Vein Detection
- Execution of Transfer Non-negative Matrix Factorization for Vein Recognition
- Advance Hand Vein Pattern Verification using Iterative Deep Neural Network
- Enhanced Reference Points Graph Matching for Partial-Obstructed Dorsal Hand Vein Identification System
On the whole, our research services are reliable and high-quality in each project of hand vein recognition system. And also, we develop your project in advanced implementation tools and technologies. If you also require a project dissertation, then we provide that too. Since we have a team of experts in every phase of hand vein recognition Python research experts. You can connect with us either for whole research services or specific phase services. Our ultimate goal is to give the best guidance in the hand vein recognition research field for both scholars and final year students.