The term finger vein recognition refers to the recognition of the human finger veins which are overlays under the skin. It uses biometric forms to authenticate humans’ identities. A biometric form utilizes pattern recognition methods to perform finger vein recognition. “This article consists of interesting and essential concepts of finger vein recognition based on deep learning with crystal clear points”
Finger vein recognition ensures security in the confidential areas of application. Mainly it is used to identify and to acknowledge the individuals. In recent, we are using so many smart gadgets such as mobile phones, iPods and so many applications. As you know that they each are protected with some safety measures like lock patterns and thumb impressions. This is one of the instances of finger vein recognition.
At the end of this article, you would become a master in this area by sailing with us throughout the article. Now let us start this article with an overview of the finger vein technology for your better understanding. Shall we get into the technical points? Come let’s have the quick insights!
What is Finger Vein Technology?
- Near-infrared lights in the finger vein detecting devices identify the individuals
- Human finger veins & fingerprints consisted database is used for finger vein recognition
- Finger vein technology uses pattern recognition methods with deep learning and machine learning
This is a crisp overview of the finger vein technology. Biometric authentication is the major key function of finger vein technology. This technology is pillared out by some of the finger vein recognition methods. Yes, we are going to enumerate the finger vein recognition methods with clear hints for ease of your understanding.
Finger Vein Recognition Methods
- Pre-processing
- Lone Diagonal Sort
- Moveable Block AHE
- Multichannel Wiener Filter
- Region of Interest Extraction
- Bresenham Line
- Histogram Tail Threshold
- Adaptive Opening Filter
- Feature Extraction
- Curvelet & Curvature
- Local Direction Pattern
- Feature Recognition
- Residual CapsNet
- Double GAN
- Two Direction-LDA
The above listed are the various finger vein recognition methods that are commonly used. As you know that every technology is facing research gaps in the form of incapabilities. Similar to every technology presented finger vein technologies also face research gaps. Yes, you people guessed right! We are going to cover the next section with the research gaps of finger vein recognition.
In addition to the notes, we would like to remark about us here. In fact, we are the only concern who is dynamically performing each and every approach of finger vein technologies. By conducting regular experiments in these areas offers us the best results compared to others. Our technical team is filtered out from the unique skill sets which are standing out from others in the industry. Now let us have the insights on research gaps in finger vein recognition.
Research Gaps in Finger Vein Recognition
- Human finger images consist of numerous finger veins, artifacts & messy shades
- This is generated by the human muscles, bones and causes the blurred finger translations
- Ineffective finger vein samples result in finger rotation dissimilarities & ROI oversights
Thus it is important to reduce the artifacts (noises) and to enrich the images given as input. These are some of the research gaps involved in finger vein recognition. In other words, it needs effective preprocessing techniques to remove the artifacts, extract the features, and enrich the features given. It has to be done before the application of distance matching methods.
The slight modifications in these are can possibly diminish the accuracy of the finger vein recognition. On the other hand finger vein recognition deep learning needs fewer preprocessing techniques as they are overlays the processes in their multiple layers.
As this article is titled with the finger vein recognition based on deep learning, we will let you know the importance of using deep learning for finger vein recognition for ease of your understanding.
Why use Deep Learning for Finger Vein Recognition?
- Deep learning automatically learns the features from the raw logs (pixels)
- It significantly reduces the finger vein recognition processing time
It robotically matches even from
- misalignments & noises
- Supervised discrete hashing & CNN methods reduce the template sizes up to 2k bits
These are the major uses of deploying the deep learning methods into finger vein recognition. Deep learning methods excellently perform the various processes. In finger vein recognition feature extraction is one of the major criteria which are first to be done. Compared to conventional methods, deep learning methods don’t require handwritten scripts to match the features. Conventional methods need handwritten scripts to extract the features like Gabor filters & curvature etc.
Conventional approaches need a huge time duration to process the finger veins. Convolutional Neural Network (CNN) is one of the deep learning methods which can perform superiorly as mentioned above. In this regard, we would like to transfer our knowledge in the fields of how does deep learning works for finger vein recognition for your better understanding.
Steps involved in Finger Vein Recognition based on Deep Learning
- Step 1
- Deep learning acquires the images of fingers
- Step 2
- Recognizes the Region of Interest (ROI) & compresses in pixels of 224*224
- Step 3
- Identifies the differences between the input and output images
- Step 4
- Offers CNN based finger vein results/outcome
These are the 4 main steps that have to be practiced in every finger vein recognition. Accurateness of the finger vein recognition is based on effective feature extraction methods. As the matter of fact, our technical experts are proficient in finger vein recognition based on deep learning methods. If you are facing any hindrances in these areas you could have an interaction with our technical crew. In fact, they are always welcoming the students to transfer their knowledge to make them wise in the emerging technologies.
Despite this, doing projects and researches on finger vein recognition deep learning would yield you the best results to be sure. As this is one of the growing technologies it is important to know about the algorithms used in these processes. Yes, we are going to showcase the popularly used deep learning algorithms for finger vein recognition. Are you interested to know about that? We know that you are very curious about them!!! Come let’s have that section with clear bulletin points.
Popularly used Deep Learning Algorithms for Finger Vein Recognition
- DCELM- Deep Conventional Extreme Learning Machine
- Gaussian probability methods used for local connection sampling
- Able to support any type of image formats (2D and 3D)
- Does not differentiate the classes accurately
- RNN- Recurrent Neural Network
- RNN learns the sequences & distributes the weights over the neurons
- It recognizes the finger veins by state of art in neural network
- Gradient vanishing causes the need for huge datasets
- CNN- Convolutional Neural Network
- Convolutional filters convert the 2D images to 3D
- It is the fastest method that recognizes the finger veins exactly
- It requires huge labeled data for the classifications
- DNN- Deep Neural Network
- DNN is consisted of more than 2 layers & permits nonlinear connections
- Regression & classification is done with this algorithm
- It offers the utmost accuracy in each process
- DNN learns very slowly & errors are retained in the former layers
The above listed are the popularly used deep learning algorithms for finger vein recognition. As we said that we are well versed in handling the deep learning methods for finger vein recognition, here we are going to cover the next section with the different methods of deep learning to recognize the finger veins and fingerprints of human beings. Shall we pass on to the next section guys? Here we go!!!
Deep Learning Methods for Finger Vein Recognition
- Two-Channel Network Learning
- Image Format: BMP
- Image Resolution: 128*64 & 480*640 Pixels
- Subjects: 106 & 100
- No.of Images: ~3816 (SDUMLA) & 6000 (MMCBNU)
- Convolutional Neural Network
- Image Format: BMP
- Image Resolution: Nil
- Subjects: 20
- No.of Images: ~1200
- DNN + P – SVM
- Image Format: BMP
- Image Resolution: 240*80 & 240*50 Pixels
- Subjects: 105 (A) & 123 (B)
- No.of Images: ~2520 (A) & 5904 (B)
- Normalization + DCNN – HM
- Image Format: BMP
- Image Resolution: 512*384 Pixels
- Subjects: Nil
- No.of Images: ~5000
- Patch – DNN + P – SVM
- Image Format: BMP
- Image Resolution: 513*256 & 480*640 Pixels
- Subjects: Nil
- No.of Images: ~2520 (A) & 5904 (B)
- Fully Convolutional Network
- Image Format: BMP
- Image Resolution: 150*50 & 146*39 Pixels
- Subjects: 123 (USM) & 105 (HKPU)
- No.of Images: 5904 (USM) & 2520 (HKPU)
The foregoing passage has revealed to you the various deep learning methods in which they are performing according to several parameters. We hope that you are getting the points as of now listed. If you still need any clarifications in these areas feel free to approach our researchers. We will give real-time illustrations with graphical visualizations if needed.
On the other hand, there are some finger veins datasets are being used to recognize the fingerprints. Yes, we are going to cover the next section for those who are not aware of this section. It is never late to learn something! Make yourself in progress and let the world by your innovative perceptions in finger vein technology. Next, we can have the datasets section.
Finger Vein Recognition Datasets
- UTFV
- Format: .BMP
- Image Resolution: 240*320 Pixels
- Image Counts per Finger: 6
- Finger Counts per Person: Ring, Middle & Index (2*3)
- Number of Persons: 106
- Number of Images: ~3816
- SDMULA-HMT
- Format: .PNG & 8 Bit Gray Scale
- Image Resolution: 380* 672 Pixels
- Image Counts per Finger: 4
- Finger Counts per Person: Ring, Middle & Index (2*3)
- Number of Persons: 60
- Number of Images: ~1440
In the previous passage, we’ve itemized the major datasets being majorly used in the finger vein recognition for the ease of your understanding. Besides, it is important to analyze the performance of the finger vein technology to ensure the applied methods are worthy or not. In fact, there are some aspects involved in the performance analysis of finger vein recognition. Come let us try to understand them with clear points.
Performance Analysis of Finger Vein Recognition
Performance analysis is dealing with several terms, and primarily we will see them first.
- ROC- Receiver Operating Characteristic Curve
- FAR- False Accept Rate
- FRR- False Reject Rate
- FMR- False Match Rate
- FNMR- False Non-Match Rate
- EER- Equal Error Rate
- FTE– Fail To Enroll
- FTA– Fail To Acquire
- FTC– Fail To Capture
These are terms that get involved in the performance analysis of finger vein recognition. Performance analysis helps us to recognize the deep learning methods whether fitted or not. Performance parameters of the finger vein aspects applied into the templates of the datasets. Let’s have further explanations in the immediate section.
- ROC automatically signifies the equilibrium between the FRR & FAR
- Matching algorithms use the threshold to take decisions
- Thresholds result in the finger vein recognition like,
- Threshold = FMR/FAR & FNMR/ FRR
- Higher Threshold = FNMR/ FRR & FMR/FAR
- EER is obtained from the ROC
- FTE indicates the unsuccessful enrolments of users in FVR
- FTC & FTA signifies the biometric sensors failed attempts in capture samples
This is how the finger vein recognition’s performance is analyzed. So far, we have discussed finger vein recognition based on deep learning concepts with brief explanations. We hope that you would have understood the things listed. If you are getting any doubts in pattern recognition areas you could reach our experts to get incredible benefits.
“Let’s impress the technical world with your different perceptions and ideologies by holding our hands”
