Unlocking Fingerprint Intelligence: Extracting Ridge, Minutiae, and DWT Features | Chapter 7 | New Horizons of Science, Technology and Culture Vol. 2

A fingerprint image captures the unique spatial pattern of ridges and valleys on the human fingertip, serving as a powerful and widely adopted A fingerprint image is a digital representation of the intricate and unique spatial configuration of ridges and valleys found on the human fingertip. These patterns are distinct for every individual and remain virtually unchanged throughout a person’s life, making fingerprints one of the most reliable forms of biometric identification. Their inherent individuality and permanence have made fingerprint recognition systems indispensable across a wide range of applications, including forensic investigations, national identity verification programs, secure access control systems, and personal device authentication.

 

This chapter, inspired by advancements in biometric sciences, anthropometry, and computational pattern recognition, investigates refined and more efficient methods of extracting rich and diverse features from fingerprint images. The emphasis lies on enhancing the accuracy and reliability of classification and identification tasks by focusing on extracting ridge information, minutiae patterns, and Discrete Wavelet Transform (DWT) features. These advanced features not only facilitate accurate fingerprint matching but also enable the derivation of soft biometric indicators such as gender, age, and potentially even blood type. This opens up promising avenues for the development of lightweight, non-invasive, and cost-effective biometric classification systems, which are particularly valuable in resource-constrained settings.

 

Fingerprint-based systems offer several key advantages over other biometric modalities, such as iris scans, facial recognition, or voice analysis. They typically require less storage space, involve simpler data acquisition procedures, and demand relatively low computational resources. These attributes make fingerprint recognition an ideal candidate for large-scale biometric applications, especially in densely populated or economically limited regions. The methodology explored in this chapter revolves around an automated framework that systematically analyses the spatial and structural features of fingerprints. It leverages both spatial domain and frequency domain analysis techniques to achieve a high-fidelity representation of fingerprint traits. By focusing on the precise extraction of ridge flows and minutiae points such as bifurcations and endings, and further enriching this representation with wavelet-based descriptors, the approach ensures robustness across various acquisition conditions, including variations in scanner types, resolutions, lighting conditions, and finger orientation.

 

Unlike traditional systems that rely solely on features such as ridge counts, thickness, and basic minutiae, the proposed approach employs enhanced feature extraction strategies that significantly improve identification accuracy. Through the application of Discrete Wavelet Transform, fingerprint images are analysed at multiple resolutions, enabling the capture of both global and fine-grained local details. This multiresolution capability allows the system to identify subtle fingerprint variations that might otherwise go undetected, making the overall classification more precise and resilient. Further, ridge structure is assessed in terms of quantifiable metrics like minimum, maximum, and average ridge lengths across the fingerprint. These measurements add another layer of distinguishing information, especially useful in scenarios where individuals have similar minutiae layouts but different ridge formations. In addition to these features, a rich set of minutiae descriptors-such as the number of ridge bifurcations, ridge endings, and total minutiae points-is extracted to enhance the discriminatory capability of the system.

 

By integrating these diverse features-spatial, geometric, and frequency-based-into a cohesive fingerprint recognition pipeline, this chapter presents a powerful and holistic approach to automated fingerprint classification. The comprehensive feature representation facilitates accurate and efficient matching of test fingerprint samples with stored templates, making the system suitable for use in high-security environments, law enforcement databases, and scalable authentication solutions.

 

In essence, this chapter contributes to the ongoing evolution of biometric technologies by introducing refined feature extraction techniques that enhance the reliability and versatility of fingerprint-based systems. The emphasis on improved accuracy, computational efficiency, and adaptability underscores the relevance of these techniques in shaping the future of secure and intelligent biometric identification.

 

Author(s) Details

 

Sayed Abulhasan Quadri
SECAB Institute of Engineering and Technology, Vijayapura, India.

 

Chandrakant P. Divate
SECAB Institute of Engineering and Technology, Vijayapura, India.

 

Tabasum Guledgudd
SECAB Institute of Engineering and Technology, Vijayapura, India.

 

Sayed Abdulhayan
PACE College, Mangalore, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v2/5744

Elucidation of Visual Motor Imagery EEG Data Using Feature Streamlining with Machine Learning | Chapter 8 | Mathematics and Computer Science: Contemporary Developments Vol. 9

Visual motor imagery (VMI) is a cognitive process where individuals visualize performing a motor task without actual movement. Electroencephalography (EEG) data is often used to analyze brain activity associated with VMI. However, the high dimensionality and complexity of EEG data pose challenges for effective analysis. This study focuses on streamlining EEG features using advanced machine-learning techniques to enhance the accuracy and efficiency of VMI classification. We applied feature selection and extraction methods to identify the most relevant features from EEG signals, followed by the implementation of various machine learning algorithms to classify VMI tasks. Our results demonstrate that feature streamlining significantly reduces computational load while maintaining high classification accuracy. The streamlined features, combined with optimized machine learning models, provide a robust framework for interpreting VMI-related EEG data, paving the way for improved brain-computer interface (BCI) systems. This approach has potential applications in neurorehabilitation, cognitive training, and enhancing user interaction in BCI technologies. Classifying motor imagery in the EEG is crucial for identifying serious illnesses. The diversity of the motor imagery EEG data hampered the rate of categorization. The motor imagery EEG classification rate is increased using the feature optimization procedure. A deep neural network-based classifier for motor imagery EEG categorization was suggested in this article. The design deep neural network is a three-layer neural network model that incorporates the teacher learning-based optimization and feature optimization technique. The EEG data's noise and artefacts are reduced using a teacher learning-based optimization technique, which also enhances the input vectors for DNN. The suggested approach has been tested on datasets from the third and fourth BCI competitions and has been simulated in MATLAB settings. The evaluation's findings indicate that the suggested technique is quite effective at compressing the current motor.

 

Author (s) Details

Virendra Kumar Tiwari
Lakshmi Narain College of Technology (MCA), Bhopal, India.

 

Priyanka Singh
Lakshmi Narain College of Technology Excellence, Bhopal, India.

 

Vinita Keer
Lakshmi Narain College of Technology Excellence, Bhopal, India.

 

Gaurav Kishor Saxena
Lakshmi Narain College of Technology (MCA), Bhopal, India.

 

Sandeep Dubey
Lakshmi Narain College of Technology Excellence, Bhopal, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v9/3416

Feature Extraction and Use of Artificial Neural Networks for Classification of PPG Signals | Chapter 17 | Innovative Solutions: A Systematic Approach towards Sustainable Future

Recent studies associated to IoTs and health sector show that equine heart rates can be obtained from the ventral midline using a non-invasive technique called photoplethysmography (PPG). It is measured with infrared light-emitting diodes and photodetectors and is associated with the pulsatile volume of blood in tissue. PPG is becoming increasingly commonly utilised as a result of developments in optical technology. The physical properties of blood vessel walls and artery pulsatile function are impacted by ageing and cardiovascular events. Techniques assess the mechanical characteristics of arteries, but their broad clinical use and utility as risk stratification tools are limited by their theoretical, technical, and practical shortcomings. This study tries to evaluate the possibility of classification of PPG signals using Artificial Neural Networks (ANNs) into different clinical classes or states and the best possible combination of features that can be used for such classification. During the study PPG signals from 35 subjects or individual, each with 50–60 signals and 300 samples (6 seconds of recording) at a 50 sample/second sampling rate, make up the dataset for analysis. During the study we observed the use of different signal features like mean, root mean, mean peak height, mean time duration between peak etc. helped to classify the PPG signals into different clinical states. The results of the classification using ANNs demonstrated that ANNs were useful in differentiating between five states. Every ANN model evaluated during the training phase demonstrated significant validation accuracy (> 98 %), and also every ANN model tested during the testing phase demonstrated significant testing accuracy (> 97 %). The results of the study indicate that the use of the given features included in the study along with the ANNs models can have a huge potential in effective classification and detection of possible clinical states of heart functions or cardiovascular functions, which finally can help in early detection and medication of possible anomaly in ideal functions.

 

Author (s) Details

 

Rubhan Borgohain
Department of Physics, Dibrugarh University, Assam, India.

 

Rajdeep Mazumdar
Department of Physics, Dibrugarh University, Assam, India.

 

Nimisha Dutta
Department of Physics, Dibrugarh University, Assam, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-47-3/CH17

The Promises of AI on Radiomics for Medical Research and Its Implementation Framework | Chapter 1 | Research Highlights in Science and Technology Vol. 4

 Medical research has currently been considerably benefited from the radiomics approach. Using radiomics allows for noninvasive belief of the pathology of tumor metastases before the collection of data consistently obtained subsequently surgery, which supports an early prediction of the effect. This study sheds light on the implementation of radiomics in healing research. This paper outlined the main parts of the radiomic framework, which involve image purchase, data collection and stowing, image separation, feature extraction, feature selection, and dossier analysis. Moreover, it expressed the implementation steps for applying machine intelligence and deep neural network algorithms to radiomics. As a result of utilizing deep neural networks, promising results have happened obtained. As a result of this work, scientists should be aware of all mechanics issues in the radiomics framework that concede possibility affect the extraction of radiomics.

Author(s) Details:

Fatma Alshohoumi,
Department of Computer Science, College of Science, Sultan Qaboos University (SQU), Alkouth, Oman.

Abdullah Al-Hamdani,
Department of Computer Science, College of Science, Sultan Qaboos University (SQU), Alkouth, Oman.

Please see the link here: https://stm.bookpi.org/RHST-V4/article/view/10907

Fault Verdict in Multi Phase Induction Machine using Intelligence Evolution Computation Algorithm Optimized Neural Network | Chapter 8 | Technological Innovation in Engineering Research Vol. 3

This research presents a new solution strategy for recognising defects in a multiphase induction motor using the least mean square filter (LMS) and a new hybrid neural network with mind evolution computation algorithm. The use of an artificial neural network (ANN) has shown to be a useful tool for solving difficulties in a variety of fields. Teaching an artificial neural network (ANN) is one of the most complex processes in system learning, and it has lately attracted a huge number of researchers. The proposed hybrid fault diagnostic technique includes an efficient feature extractor based on LMS and a fault classifier based on a hybrid neural network. Testing 600 samples modelled on the failure model determines the performance and efficiency of the offered hybrid neural network classifier. The average correct classification with and without the mind evolution computation technique is roughly 98 percent and 96.17 percent, respectively, for various defect signals. The findings of the simulation research show that the proposed hybrid neural network is effective for fault detection in multiphase induction motors.

Author(s) Details:

A. Balamurugan,
Department of Electrical and Electronics Engineering, Ariyalur Engineering College, Ariyalur, Tamil Nadu, India.

R. Ramya,
Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, Tamil Nadu, India.

S. Saravanan,
Principal, Ranipet Institute of Technology, Walaja, Vellore, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/TIER-V3/article/view/7065

Attendance Capturing and Consolidation Mechanism Using Image Processing and Machine Learning Technique | Chapter 11 | Innovations in Science and Technology Vol. 5

 

Microwave ovens had a crucial role in the twentieth century. This frequency also sees a lot of application development. Microwave tube advancements have aided numerous growths in the recent past. Based on the RF field phase velocity, microwave tubes can be classed as slow-wave or fast-wave devices. A high-power source and amplifier are necessary for millimetre and sub-millimeter wave applications, which can be obtained by designing fast-wave devices. The interaction of the electron with the RF wave in fast wave tubes differs from slow-wave devices, as will be explained further. A brief history is discussed, as well as the many types of fast-wave tubes and their dispersion relationships.

Author(S) Details

Rajib Bag
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India.

Gautam .
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India

Komal Das
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India.

P. Mohini Amma
Department of Computer Engineering, Dr. B.R. Ambedkar Institute of Technology, Port Blair, Andaman & Nicobar Islands, India.

View Book:- https://stm.bookpi.org/IST-V5/article/view/5706

EEG Bases Emotion Detection Using Deep Learning Algorithm | Chapter 9 | New Approaches in Engineering Research Vol. 16

 The use of physiological markers to identify human emotion states is a rapidly emerging area of research in human-computer interaction. Gaming Disorder has become a major topic of concern in recent years, piqueing the curiosity of experts who want to learn more about it. The goal of this study is to use electroencephalography to detect the emotional behaviour of people who play online games on a regular basis (EEG). EEG is a prominent method for investigating addictive behaviours that is low cost and has a good temporal resolution. As a result, the EEG will be a good predictor of the subject's emotional state. The current study uses the SEED-IV data source to build a model for emotion states, and obtained signals are used to investigate gamming addiction behaviour. The VGG pre-trained model is supplied the spectrogram features. On the SEED-IV database, the trained model has a prediction accuracy of 89.54 percent and a testing accuracy of 78.63 percent. The acquired signals are checked against the trained model, yielding a 75 percent accuracy.

Author(S) Details

S. Thejaswini
Department of Electronics and Telecommunication, B M S Institute of Technology & Management, India.

N. Ramesh Babu
Department of Computer Science Engineering, Amruta Institute of Engineering & Management Sciences, India.

K. M. Ravikumar
Oxford College of Engineering, India.

View Book:- https://stm.bookpi.org/NAER-V16/article/view/4062

Hierarchical Scale-space Representational Measure for Estimating Land Cover| Chapter 11 | Current Topics on Mathematics and Computer Science Vol. 2

 Shape-filling curves such as planar lines and rectilinear segments are the Minimum Mapping Unit (MMU) for an object-oriented image analysis operation. When the scale is changed, the space-filling curves do not change the feature object representation, thus representing spatial and aspatial features with finer or coarser granularity. The increased collinearity can be explained by the arrangement of topological objects (neighbors/objects) in the aggregated feature space, which results in image areal objects. Instead of performing a single operation on the imagery objects by scanline rows, we can compute on custom built algorithms applied to distinguishing objects. This operation produces super. relationships, taking advantage of multi-scale object-oriented analysis procedures A continuous hierarchical scale space filtering operation is adapted for segmentation purposes for information retrieval. In fact, MMU variations will generate instances of image objects that retain the spatial scale at a given optimizing parameter. This article focuses on object-oriented analysis and fuzzy inference analysis of the imagery scene. By denoting image analysis procedures based on image objects at the characteristic scale, imagery semantics at the low and how-level spatial context can be delineated. With object oriented scale space hierarchical theory and varying intra and inter scale parameters, such a method becomes feasible. Fuzzy modeling of mixed pixels is used to extract reliability without incorporating edges while using image objects to calculate multi-variate statistics (Entropy measure, heterogeneity measure, local mean vs. local variance measure, and mean vs. covariance measure). The Region Labeling Operator will use in-class variance measures to resolve homogeneous areas of mixed pixels. In between classes Variances can be used to calculate the distance between scale intervals that the scale object can resolve. This results in a hierarchical network that delineates the final object's features further. In the Region Growing and Region Merging procedures, the Scale Operator (SO) is defined as the varying optimizer selection. Individual objects with similar sub-class variance and texture characteristics will be fused to create a segmented super object during the region abstraction process. The Scale Operator diffuses the super-objects as a result of the increased heterogeneity, and thus more objects are merged and created within the class intervals.

Author (S) Details

C. Rajabhushanam
Computer Science Engineering, BIST Bharath Institute of Higher Education and Research, Chennai, India.

View Book :- https://stm.bookpi.org/CTMCS-V2/article/view/1783

Extraction of Wavelet Features for the Classification of Sleep Stages Using Single Channel EEG | Chapter 15 | Recent Developments in Engineering Research Vol.2

 

Sleep is just as important as diet and exercise. Humans spend about one third of their lives asleep.
Sleep tests involves processing and analysis of many signals combination called as
Polysomnographic signal (PSG). In the large data sets like Sleep Electroencephalogram (Sleep EEG), to do analysis it becomes tedious and time taken. Instead of considering the whole data, considering a few critical features from the signal makes the analysis simpler and the memory requirements are also less, since the analysis could be carried out on digital platform. A feature is a distinguishable sectional property obtained from a portion of signal. Feature extraction depicts the number of feature to be extracted from the signal. Thus the feature extraction plays a pivotal role in the analysis of Sleep EEG. In this work we discussed the decomposition of Sleep EEG signal into required frequency bands and adopted feature extraction techniques of wavelet decomposition method to extract features from Sleep EEG signal by considering single channel EEG.

Author (s) Details

Vijayakumar Gurrala

Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.

Padmasai Yarlagadda
Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.

Padmaraju Koppireddi
Department of Electronics and Communication Engineering, JNTU Kakinada, Kakinada, Andhra Pradesh, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/236