Biometric Identification systems are widely used for different identification. Especially for fingerprint recognition system by neural network’s verification and identification. There are many types of biometric systems such as fingerprint recognition, voice recognition, facial recognition, palm recognition, iris recognition, etc. In the midst of all of this, Fingerprint detection is one of the most well-known and widely used biometric technologies. Fingerprints are a pattern of hills and valleys on the surface of the fingernail. The last places and crossings of the crowns are called minutiae. It is a widely accepted idea that the minutiae pattern of each finger is different and does not change during a lifetime. The end of the Ridge is the point where the curve of the line ends, and the bifurcations are where the ridge separates from one lane to the two lanes at Y-junction.
The removal of fingerprint technology is applied to images made of different layers on your hands. Fingerprints are taken with a scanner and enhanced and then converted into a template. Scanning technology can be optical, silicon, or ultrasound technology. Optical scanners are the most widely used and simple. There are two types of fingerprint recognition methods. The first is minutia – based on representing fingerprints with its local features, such as finishes and split ends. The second method uses the same image-based methods using the global features of the fingerprint image.
In this post, we will discuss the Fingerprint Identification System using Neural Networks. The neural network is also known as the corresponding neural network. It is a computer program that mimics freely in our brain structures. It consists of interconnected substances called nodes or neurons that work together to produce the output function. These nodes work together to produce the output function. Neural network depletion depends on the interaction of individual neurons within the network. The processing of information on neural networks is done in the same way and not in a series like previous binary computers or Von – Neumann machines.
It is widely known to develop a reliable fingerprint system, image enhancement and feature extraction are required. The proposed algorithm is divided into three main sections: Pre-configuration, post-processing, and final component alignment. Advancing the phase included image enhancement using histogram equalization, binarization, and morphological performance after using this enhancement algorithm to obtain a reduced image. In the second stage, the minutiae are extracted from the developed fingers using a fine-tuning process. The final stage is the recognition of fingerprints performed with the help of the neural network.
The algorithm does the following:
A. Calculates gradient values according to x – and y-direction – for each block pixel. Two Sobel filters are used to accomplish the task.
II. For each block, it uses the following formula to find the square measurement of at least one block indicator.
tan2ß = 2 ∑ ∑ (gx * gy) / ∑ ∑ (gx ^ 2 – gy ^ 2) of all pixels in each block.
Many methods are used to find the fingers. Among them, the style of writing with text remains the most popular method. Empty finger scanners are also available that complete the process of the centralized computer installation. Finger quality is very important because it directly affects the minutiae extraction algorithm. The size of the scanned fingerprints used in this study was 188 × 240 pixels. Images are taken in size to reduce computational load.
Fingerprint processing is required to:
(i) improve the clarity of the fingerprint structures
(ii) maintain their integrity,
(iii) to avoid the introduction of false buildings or art objects, and
(iv) maintain boundary communication while maintaining boundary differences. Fingerprint processing functionality is an image enhancement, image familiarity, and duplicate image
It makes the image clearer by continuing to work. Fingerprints obtained with sensors are not of perfect quality, so increasing the difference between the grooves and the grooves and connecting the false points to improve the crowns is done. To improve, using the FFT development method. In this case, we split the image into smaller processing blocks of 32 * 32 pixels and make a Fourier transformation.
Ridge Fingerprint Reduction:
The Thinning Ridge is made to eliminate unwanted ridges of ridges until the bars are one pixel wide. In this case, the iterative parallel thinning algorithm is used. In each image scanner, the algorithm marks unwanted pixels in a small image window and eventually removes all those marked pixels after several scans. The Ridge map was reduced and filtered through other Morphological operations to remove H breaks, remote points, and spikes. In this step, any single point, whether single point points or single point breaks in the community are eliminated and considered as adjusting noise. The spinal structures in the lower extremity images are not always well defined and, hence, positioning information is poorly available, which greatly hinders the use of these techniques. A process based on the Gabor filter can detect reliable image detection limitations. It is not suitable for an online fingerprint recognition system like AFIS because the algorithm is computerized.
Separation of Fingerprints:
The quality of the fingerprints can vary greatly, mainly due to the skin condition and the pressure exerted by the fingerprint on the device to feel that some kind of forwarding is required to achieve a good minutiae release. This problem can be managed using a development algorithm that can differentiate and highlight ridges in the background; this type of development is also called balancing. The binarization step is that the actual data that can be extracted from the printer simply say binary means 0 and 1; hills vs. valleys. But it is a very important step in the skull extraction process because the drawings are considered to be gray images, so the cracks, knowing that they are edges, still vary in intensity. Thus, binarization converts an image from a 256-degree image to a 2-level image that provides the same details. Typically, the object pixel is rated “1” while the rear pixel is rated “0.” Finally, a binary image is created by adding each color to white or black pixels, depending on the pixel label (black by 0, white by 1)
After the decline of the community, the point of marking the minutia is an easy task. The concept of Crossing Number (CN) is widely used to exclude minutiae in an image. Also, at this stage, the average inter-ridge width range is estimated. This refers to the average distance between two neighboring boundaries. Scan a small ridgeline. Picture and summarize all pixels in a queue for their values. Then divide the length of the line with the summary at the top to find the middle width. More precisely, such a type of line scanning is performed on many other lines and column scanning is performed, finally, the entire inter-ridge width is limited to finding a D.
Removal of False Minutia
In furtherance of false positives breaks due to an insufficient amount of ink and cross-ink ridge connection due to excess inking is not completely removed. All the previous sections themselves occasionally introduce other art objects that lead to false minutiae. Such false minutiae will greatly affect the accuracy of the simulation if they are simply considered to be real minutiae. It, therefore, needs to remove the false minutia and keep the fingerprint identification system running smoothly.
After finding two sets of converted minutiae points, we use Neural Networks to calculate the corresponding minutia by assuming that two minutiae have almost the same shape and direction.
Backpropagation is one of the various learning algorithms that can be used for neural network training and is used in this concept. It belongs to the so-called learning class. For all the input vectors introduced into the neural network, there is a previously defined network response in the vector t (teacher). The desired output of the neural network output is then compared to the actual output by an error in e vector t and neural network output vector y. Weight correction in the neural network is done by spreading the error backward from the output layer to the input layer, hence the name algorithm. The weight conversion in each layer is done with a very advanced algorithm. The retrieval algorithm is performed in the following steps:
- Select a training pair from the training set; use the network input vector.
- Calculate network output.
- Calculate the gap between the network output and the required output (vector detected from the training pair)
- Adjust network weights in a way that minimizes error.
- Repeat steps 1 to 4 for each vector in the training set until the error of the entire set is reasonably low.
- During the training phase, training data is entered into the installation layer. The data is distributed in a hidden layer and then the output layer. This is called the continuation of the back distribution algorithm. The output volume of the output coefficient is compared to the output values. Targeted release rates are those that we are trying to educate on our network. The error between the actual output values and the specified index values is calculated and distributed back to the hidden layer. This is called bypassing the back-end distribution algorithm. Error is used to restore connection power between nodes, e.g. Weight metrics between input – hidden layers and hidden output layers are updated.
CNN is an artificial neural network that contains different neurons or cells. It has several areas that can be useful compared to other neural networks. The powerful range of CNN, for example, is tied. CNN can be easily expanded without having to reorganize the entire network because the cell is not connected to all the other cells in the network but rather localized cells. Although its cellular structure, it still exhibits a complex character as seen by other neural networks. Due to this complex behavior, it can be used in image processing (e.g. noise removal, connected object detection (CCD), ‘diminishing’ etc.). The status and output vary from time to time, and input is always maintained. The templates describe the interaction of a cell with its neighbor and control the emergence of CNN status and output vectors. Template connections can be detected by generators powered by electricity. The detection feature is the sigmoid-type function of the line.
Another advantage is that although an object whose cell output affects the functioning of other cells (template parameter) may differ from the neighboring cells geographically, the template built in such a way is consistent with the translation (‘merging elements’). As mentioned above, these cells can interact with other cells locally. CNN is a two-rectangular rectangle with 16 cells where each cell interacts with its neighboring cells only. CNN is exploited by image processing by combining each image pixel input or the original state of a single cell. After that, both the state and the release of the CNN matrix shifts to a state of equilibrium. The appearance of CNN is dominated by template selection. Many templates have already been defined to perform basic image processing tasks. The easy operation can be done using basic A, B, and bias I templates, and more complex processing requires the use of offline templates and a generalized nonlinear generator