Mathematical Morphology Research Articles

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.

Height estimation of sugarcane tip cutting position based on multimodal alignment and depth image fusion

Sugarcane tip cutting is essential to reducing the rate of impurities in the harvest. To achieve adaptive regulation of the tip-cutting position by a sugarcane harvester, we propose a method for estimating the height of the tip-cutting position of sugarcane. The RGB and Binocular depth cameras are aligned to process the sugarcane tip region image. This involves threshold segmentation, morphological operations, and contour detection to identify the tip-cutting position and upper boundary contours. The depth image is segmented using contour pixel information and merged to form a colour depth image of the sugarcane's tip. This image is then transformed using depth data and triangular parallax principles to determine the height of the sugarcane tip-cutting position. The proposed method was evaluated in various sugarcane plantation environments. Comparative analysis between the proposed method and manual measurements of actual cutting position heights revealed that the RMSE ranged from 1.22 cm to 1.78 cm, and R2 varied between 0.79 and 0.86. These results demonstrate the effectiveness of the proposed method in accurately extracting the height information of sugarcane tip-cutting positions, which has a specific application value for the adaptive adjustment of the tip-cutting device of the sugarcane harvester.

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

Objectives: The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. Methods: In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which includes Gaussian filtering for noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance image features. In the second step, we identify tumor-related clusters using morphological operations and delineate the tumor regions using Active Contour (Snake) model to get a segmented image. In the final step, the Boltzmann Monte Carlo method is used to refine the edges of the segmented image. To evaluate the effectiveness of this approach, the 2D brain tumor datasets, available in the public domain, are used. The first dataset is taken from Kaggle website and has 3064 MRI human brain images and its respective ground truth images which is used for segmentation. The second dataset is used for visualization of segmented tumor, available in the same Kaggle website. Findings: The Performance metrics for finding similarity between the segmented images generated using the proposed MACB model and the ground truth images, available in the first dataset, exhibit higher values. That is, the proposed method has achieved higher values of Dice Similarity Coefficient (DSC): 93.26%, Jaccard Co-efficient: 86.44%, Sensitivity: 97.27%, Specificity: 99.43% and Pixel accuracy: 98.95%. Novelty: In this research work, MACB model is proposed for the detection, segmentation, and refinement process of brain tumor by incorporating Boltzmann Monte Carlo method with Morphological Region-Based Active Contour model. This novel approach has resulted in enhanced precision and efficiency in the brain tumor segmentation process. Keywords: Brain Tumor Segmentation, Morphological Operation, Active Contour, Boltzmann Monte Carlo Method, Magnetic Resonance Imaging

Tone sandhi in Nuosu Yi at the interface

Abstract Nuosu Yi, a Tibeto-Burman language, has a tone sandhi pattern, where a lexical [33] -tone is realized as [44]-tone on the surface. This tone sandhi pattern in Nuosu Yi is described as having a “weak phonological status (Gerner, 2013: 28)”. In this study, we confirm this observation and further show the role of phonological length in the tone sandhi patterns. A closer examination of the sandhi contexts, however, reveals an intricate interplay between phonology and morphology of various aspects of Nuosu Yi grammar. Tone sandhi is conditioned by word size and morphological processes. Monosyllabic words, but not multi-syllabic words, tend to undergo tone sandhi, and tone sandhi is sensitive to morphological operations such as compounding.

Building detection in SAR images based on fusion of classic and deep learning features

ABSTRACT Because of high-resolution imaging in day and night and any weather condition, Synthetic Aperture Radar (SAR) has various applications in remote sensing. Building detection is one of the important utilization of SAR images. With analysing SAR images information, a new framework based on fusion of statistical, texture, and semantic features is proposed. At first, in order to reduce the speckle noise effects on the classification results, the SAR image is segmented into superpixels which the classification process is performed for these segments. Then, the statistical Fisher and Haralick texture features are extracted and the best features are selected. By applying an adapted VGGNet, the third type of features are extracted. Using training samples, the features achieving the highest classification accuracy are selected and applied to the classifier. Then, the classification output is improved by decreasing the splitting of the large-size buildings and false alarms using morphological operations and contour fitting process. The performance of the proposed framework is evaluated with two different types of TerraSAR-X images of urban areas. Experiment results show that the proposed method has superior results than the similar works. Also, the proposed method has considerable small false alarm rate.

Extraction of laser stripe centerlines from translucent optical components using a multi-scale attention deep neural network

The precise extraction of laser stripe centerlines is critical for line-laser 3D scanning systems. However, conventional methods relying on threshold segmentation and morphological operations face significant challenges when confronted with pervasive optical phenomena, including specular reflection, scattering, and bleeding, which are commonly observed in translucent optical components. These methods typically require complex preprocessing procedures and often yield poor precision in centerline extraction. In this paper, we introduce a novel learning-based approach, complemented by a meticulously curated dataset, explicitly designed to address these challenges. Our proposed method leverages a multi-scale attention U-Net-like architecture, initially tasked with the segmentation of laser stripes from the complex background environment. Subsequently, it employs the Steger algorithm for the precise extraction of laser stripe centerlines. The experimental results, obtained by comprehensively evaluating real-world captured images, clearly demonstrate the effectiveness of our deep neural network combined with the Steger algorithm. This combined approach exhibits exceptional accuracy even when challenged by the interferences from specular reflection, scattering, and bleeding artifacts. Specifically, our method achieves a mean intersection over union (mIoU) of 84.71% for the laser stripe detection task, accompanied by a mean square error (MSE) of 10.371 pixels. Also, the average execution time for the centerline extraction task is notably efficient at 0.125 s.

Enhancing Brain Tumor Classification: A CNN-Based Approach with InceptionV3 and Xception

Brain tumors are among the most aggressive and deadly diseases, with a very short life expectancy at the highest grade. To combat this, early detection and treatment are crucial.In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task, and there might be possibilities of human errors. Hence, accurate analysis in a short span of time is essential. This approach presents the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN), InceptionV3 and Xception algorithm for classification of Glioma, Meningioma and No tumor. The brain part is initially segmented by a thresholding approach followed by a morphological operation. The brain MRI is classified using CNN, Inceptionv3, and Xceptionv3 algorithms. The system's performance is evaluated using precision, recall, F1 score, and accuracy parameters.

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

Solar magnetic flux rope identification with GUITAR: GUI for Tracking and Analysing flux Ropes

Modelling the early evolution of magnetic flux ropes (MFRs) in the solar atmosphere is crucial for understanding their destabilization and eruption mechanism. Identifying the relevant magnetic field lines in simulation data, however, is not straightforward. In previous work an extraction and tracking method was developed to facilitate this task. Here, we present the corresponding graphical user interface (GUI), called GUITAR (GUI for Tracking and Analysing flux Ropes), with the aim to offer a variety of tools to the community for identifying and tracking MFRs. The starting point is a map of a selected proxy parameter for MFRs, e.g., a map of the twist-parameter Tw, current density, etc. We showcase how the GUITAR tools can be used to disentangle a multi-MFR system and facilitate in-depth analysis of their properties and evolution by applying them on a time-dependent data-driven magnetofrictional model (TMFM) simulation of solar active region AR12473. We show the MFR extraction using Tw maps, together with targeted use of mathematical morphology algorithms and discuss the evolution of the system.

Anomaly detection in structural dynamic systems via nonlinearity occurrence analysis using video data

Dynamic loads in disaster events, such as earthquakes, often cause structural damages that severely affect serviceability and even reduce the safety of civil structures or render them unsafe. Most of these damage scenarios can cause the transition from a linear to nonlinear behavior in structural dynamics. Therefore, extracting such nonlinear behaviors is an effective strategy for structural anomaly detection. In recent years, the rapid development of media devices has led to the widespread use of video data, primarily because of the ease of acquisition and lack of contact measurement. This study developed a novel method for detecting anomalies due to structural nonlinearity from video data that could capture target dynamic behaviors via non-contact measurement. The method is based on optical flow, extended repulsive force networks, and morphological operations for extracting singularities due to nonlinear events in the estimated motion vector field in video data. Subsequently, shaking table tests on a three-story shear building model containing a novel controllable hinge bearing verified the effectiveness of the developed method. The results of the feature enhancement process demonstrate the accurate localization of anomalous regions and effective mitigation of unwanted interference using the proposed method. The proposed method facilitates the use of video-based technology to evaluate rapid damage conditions post-earthquake.

Signal-based fault location method using time and frequency domain

This research introduces an innovative approach to fault detection in high-voltage alternating current (HVAC) systems by integrating temporal and frequency domain characteristics of signals. It utilizes Traveling Wave (TW) detection and analysis, focusing on a precise window of just 100 microseconds surrounding the TW's arrival at the measurement point. Mathematical Morphology is applied to identify time-domain properties, while the Stationary Wavelet Transform is employed to extract frequency-domain features. Additionally, the integration of a Dynamic Mode Decomposition-based technique enhances TW detection accuracy. The efficacy of this approach is extensively assessed for fault site classification and regression tasks, demonstrating superior performance compared to utilizing time or frequency-domain features separately. The study also examines the method's resilience to noisy measurements and the sufficiency of training data. Comparative analysis against existing signal-based approaches within the IEEE 34 node system underscores its heightened accuracy. Furthermore, the proposed technique showcases minimal fault location estimate errors along the feeder length and competes favorably with slower phasor-based fault detection methods in performance. Regarding the distance of the fault, its proximity to the generator, transmission line, or load significantly influences the parameters of positive, negative, and zero sequence components. When a fault occurs, these parameters undergo distinct changes, serving as indicators of the fault's severity and location. If the fault occurs near the generator or substation, there may be alterations in the voltage and current magnitudes, affecting the sequence components accordingly. In the case of a fault in the transmission line, impedance changes can lead to variations in sequence parameters. Similarly, a fault near the load can cause disturbances in the voltage and current profiles, impacting the sequence components. Analyzing these changes in positive, negative, and zero sequence parameters during a fault provides valuable insights into the fault's severity and location, aiding in effective fault detection and mitigation strategies. Key Words – fault detection, high-voltage alternating current (HVAC) systems, Traveling Wave (TW), temporal domain, frequency domain, Mathematical Morphology, Stationary Wavelet Transform, Dynamic Mode Decomposition, fault site classification,

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries

BackgroundIn low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data.MethodsWe implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software.ResultsThe model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%.ConclusionsWe demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Image Segmentation Technology Based on Ant Colony Algorithm

Image segmentation is a key task in computer vision, with applications ranging from medical diagnosis to autonomous driving. The Ant Colony Algorithm (ACO), modeled after ant foraging behavior, has emerged as a viable segmentation methodology. However, ACO-based segmentation algorithms frequently generate segmented outputs with jagged or uneven boundaries, which reduces their interpretability and usability. To alleviate this problem, they study the use of boundary-smoothing approaches in ACO-based segmentation. In this paper, they investigate image segmentation technology based on the Ant Colony Algorithm, with a focus on border smoothing. They examine the fundamentals of ACO and its application to image segmentation, emphasizing its strengths and limits. They also look at several boundary smoothing strategies, such as morphological operations, edge-preserving filters, and active contours (snakes), and how they affect segmentation performance. Through experimental validation and comparative analysis, they show that boundary smoothing improves the accuracy and visual quality of segmented images produced by ACO-based segmentation algorithms. These results help to design more robust and visually appealing segmentation algorithms, which have potential applications in medical imaging, remote sensing, and industrial automation.

DARTS-based morphological neural network design and application in bearing fault diagnosis

Morphological filters are widely used in data pre-processing, whereas the existing morphological filters are not optimal in terms of performance, since they are designed by combining the basic morphological operators together artificially. Hence, we proposed an automatic morphological neural network design method based on differentiable architecture search (DARTS), which is a neural architecture search (NAS) method. Firstly, the mechanism is revealed that the morphological dilation with flat structural elements behaves the same as Maxpooling without down-sampling. Secondly, morphological dilation and erosion neural network layers were defined. Thirdly, using DARTS and the search space with morphological neural network layers, end-to-end morphological neural network was implemented. Finally, the morphological neural network was successfully applied in bearing fault diagnosis. Experiments on the three datasets show that the proposed method improved the feature extraction capabilities of neural networks, then achieved end-to-end bearing fault diagnosis, and the diagnosis accuracy was improved by more than 2.0%.

Principal Component Analysis–Based Feature Recognition Technology for Marine Plankton Images in IoT Systems

Abstract To improve the accuracy of traditional plankton image feature recognition techniques, a principal component analysis (PCA)–based method for plankton image feature recognition in Internet of Things (IoT) systems was designed and proposed. According to the characteristics of a microscopic image of marine living plankton with much noise, a noise suppression method based on wavelet is established to meet the needs of edge information retention and stability in image processing. At the same time, the adaptive threshold gray segmentation, mathematical morphology operation, and canny edge detection operator are combined to complete the marine plankton image segmentation. Based on the global feature and texture information of contours, the feature set is composed. At the same time, PCA is used to reduce the dimensionality of the feature space and complete the feature recognition of plankton images in the IoT system. The test results show that this method has higher accuracy, shorter recognition time, lower cost, and certain applicability.

Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.

Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis. This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest. The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots. The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.

Algorithm Based on Morphological Operators for Shortness Path Planning

The problem of finding the best path trajectory in a graph is highly complex due to its combinatorial nature, making it difficult to solve. Standard search algorithms focus on selecting the best path trajectory by introducing constraints to estimate a suitable solution, but this approach may overlook potentially better alternatives. Despite the number of restrictions and variables in path planning, no solution minimizes the computational resources used to reach the goal. To address this issue, a framework is proposed to compute the best trajectory in a graph by introducing the mathematical morphology concept. The framework builds a lattice over the graph space using mathematical morphology operators. The searching algorithm creates a metric space by applying the morphological covering operator to the graph and weighing the cost of traveling across the lattice. Ultimately, the cumulative traveling criterion creates the optimal path trajectory by selecting the minima/maxima cost. A test is introduced to validate the framework’s functionality, and a sample application is presented to validate its usefulness. The application uses the structure of the avenues as a graph. It proposes a computable approach to find the most suitable paths from a given start and destination reference. The results confirm that this is a generalized graph search framework based on morphological operators that can be compared to the Dijkstra approach.

The Detection and Classification from the Multimodal Images using Artificial Intelligence for Lung Diseases

Lung cancer is a disease in which healthy cells in the body gradually convert into tumour cells, resulting in a variety of medical issues. A standard dataset exists for lung cancer. With the rising incidence of lung cancer and the exponential growth of CT pictures, having a quick and effective way to evaluate CT scans can help physicians or surgeons develop an early treatment plan. In this research, two approaches are investigated for lung cancer prediction. One approach is based on training machine learning model on the features extracted from image processing techniques. And the other approach involves Artificial Intelligence models for lung cancer detection. Computed Tomography (CT) pictures are useful for determining the stage of lung cancer in a patient. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. For medical photos, texture analysis is critical. As a result, algorithms such as the K-Means clustering method and morphological operations such as erosion, dilation, and so on are used

PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enhanced by PSO Optimization

The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature. The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage. The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively. The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

Fingerprint Identification with Fusion of Gabor and Minutiae Features Using BPNN Classifier

This study presents an innovative approach to fingerprint identification by leveraging the fusion of Gabor and Minutiae features, employing a Backpropagation Neural Network (BPNN) classifier for accurate categorization. The process involves initial handling of a fingerprint image dataset, including crucial pre-processing steps such as resizing images and implementing morphological operations like dilation, erosion, and opening. Subsequently, Gabor features and minutiae extraction are performed, followed by the fusion of these features to create a comprehensive representation. To address dimensionality concerns, Principal Component Analysis (PCA) is applied. The dataset, comprising the fused features and corresponding labels, is then loaded for the final step - classification using a BPNN. The network is configured for feed-forward backpropagation, distinguishing fingerprint patterns into categories such as arch, left loop, right loop, tented, and whorl. The evaluation metric used to measure the success of the classification process is accuracy. This approach aims to enhance fingerprint recognition by combining distinctive Gabor and minutiae features, ultimately achieving a more robust and precise identification system through the utilization of neural network-based classification. Keywords—BPNN, PCA, Finger Print Images Dataset