Biometric Pattern Recognition Research Articles

Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach

The stability of power grids is critical in ensuring the consistent and efficient delivery of electricity. However, traditional predictive models often fall short in addressing grid data's intricate and ever-changing nature, making it challenging to maintain grid reliability. This paper introduces a novel hybrid approach that combines Biometric Pattern Recognition (BPR) with Long Short-Term Memory (LSTM) networks to enhance the prediction of smart grid stability. This approach employs BPR techniques to extract essential features from smart grid data by leveraging the pattern recognition capabilities typically used in biometric systems. These techniques are particularly effective in identifying and isolating the most relevant patterns within the complex datasets generated by smart grids. On the other hand, the LSTM-based model is designed to handle the temporal dependencies and nonlinear patterns characteristic of grid data. LSTMs, known for their proficiency in time-series analysis, are well-suited for capturing the sequential nature of grid data, enabling more accurate predictions over time. Integrating BPR and LSTM in this hybrid model addresses several limitations in existing predictive methods. By combining the strengths of both techniques, the model enhances the accuracy of predictions and improves the overall reliability of grid stability assessments. Extensive experiments were conducted using real-world datasets to validate the effectiveness of the proposed hybrid model. The results demonstrate a significant improvement in prediction accuracy, with the BPR-LSTM model achieving a 98.25% increase in accuracy compared to traditional prediction methods. This improvement underscores the potential of the BPR-LSTM hybrid approach to play a pivotal role in advancing the stability and reliability of smart grid systems.

Facial Recognition Based Automated Door

To ensure the accuracy and efficiency of intruder identification, the proposed method is combined with Haar clssifier technology for face detection. When someone comes to the door, the Pi camera captures the image and starts the face detection process. In this research, we implement a facial recognition component to capture human images, comparing them with stored data in a database. Upon a match with an authorized individual, the system unlocks the door through an electromagnetic lock. The demand for a rapid and accurate face recognition system persists, continuously evolving to swiftly identify intruders and restrict unauthorized access to highly secure areas, thereby reducing human error. Facial recognition stands as a crucial component within secure systems, surpassing biometric pattern recognition methods, and finds widespread application across various domains.

A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the performance of the multimodal contactless biometric pattern recognition system. The most significant advantage of palm vein and palm dorsal vein recognition is that it provides relatively high accuracy, reliability and also liveness detection. In this recognition process, the palm and its dorsal vein images are segmented using multilevel segmentation of region of interest (ROI) depending on Adaptive Hyper Kernel Optimized Fuzzy Clustering Technique (Adapt-HKFCT), and then ROI extracted based on the valley points among the index and ring finger using the Knuckle points. Then, the features of the ROI amid the index and ring finger are extracted using centre-symmetric local binary pattern algorithm and fed to the Chaos-DNN for training and classification. The efficiency of the features is optimized using SPOA. The extracted feature points help in authenticating a person using his/her chosen biometric traits for security purposes. The objective of this system is to increase the efficiency of the biometric system, and the performance is calculated depending on equal error rate, correct recognition rate, decidability index, false acceptance rate and false rejection rate. The proposed algorithm has been implemented in MATLAB platform. The experimental outcomes demonstrate that the proposed palm vein recognition depending Adapt-HKFCT with Chaos-DNN using SPOA method (PVR-AHKFCT-CDNN-SPOA) attains higher performance metrics with an accuracy of 98.3% and is also more efficient likened to the other existing methods, such as K Nearest Neighbour with Particle Swarm Optimization (PVR-KNN-PSO), Fuzzy Brain Storm Optimization based on Adaptive Thresholding (PVR-FBSO-AT), multiscale local binary pattern with ant colony optimization (PVR-MSLBP-ACO) and Deep Learning with Bayesian Optimization (PVR-DL-BO).

Face Recognition Based on Artificial Neural Network: A Review

The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

An abstract model of an artificial immune network based on a classifier committee for biometric pattern recognition by the example of keystroke dynamics

An abstract model of an artificial immune network (AIS) based on a classifier committee and robust learning algorithms (with and without a teacher) for classification problems, which are characterized by small volumes and low representativeness of training samples, are proposed. Evaluation of the effectiveness of the model and algorithms is carried out by the example of the authentication task using keyboard handwriting using 3 databases of biometric metrics. The AIS developed possesses emergence, memory, double plasticity, and stability of learning. Experiments have shown that AIS gives a smaller or comparable percentage of errors with a much smaller training sample than neural networks with certain architectures.

Identification of fingers on the basis of Hamiltonian cycles of local features

The problem of finding the lengths of Hamiltonian cycles on complex graphs is considered. The task has such practical applications as determining the optimal routes (salesman's task), identifying graph structures (recognizing the characteristics of local features of biometric objects), etc. When solving the task of verification of biometric samples, the problems of addition or disappearance of reference points, deformation of the distances between them, the appearance of linear and angular displacements of the whole sample emerges. Using the method described in the article, the problem of displacements can be eliminated, as the solution is stable when shuffling of the points is present. Moreover, it is possible to obtain reference plans with the same stability. Obtaining them requires less computational complexity and provides greater recognition accuracy. A detailed description of the problem solution based on the application of the method of branches and boundaries for symmetric matrices of graphs, which describe the distribution of local features in the images of fingerprints, has been proposed. It is known that a guaranteed solution for finding the length of the Hamiltonian cycle for an arbitrary graph of the planar distribution of points is possible only by using an exhaustive search. However, the computational complexity of such a search is not acceptable. The method of branches and boundaries, like all existing methods of directional search, does not guarantee finding a solution with an arbitrarily large dimension of the graph. Therefore, a method of decomposing graphs is proposed, which allows reducing a complex problem to a set of simpler ones. That allows for a significant reduction in computational complexity. The relative invariance of the metrics of Hamiltonian cycles to probabilistic shifts, which are characteristic of biometric pattern recognition problems, has been shown.

Bimodal biometric pattern recognition system based on fusion of iris and palmprint using multi-resolution approach

A biometric pattern recognition system is a pattern recognition system which uses the biological traits to recognize individuals. Single biometric systems may not possess the required characteristics like permanence, acceptability and circumvention. In general, the performance of the unimodal biometrics systems is degraded due to noisy data acquisition from sensor, intra-class and inter-class similarities. Several such restrictions are removed when multiple sources of information are used. In this paper, a bimodal biometric system designed from iris pattern and palmprint pattern is described. The feature extractor is created from the wavelet packet analysis, and classifier is created based on neural network. Using wavelet packets and gray-level spatial dependence matrix, the iris code vector is constructed. With the help of Gabor wavelets and gray-level spatial dependence matrix, the palmprint code vector is computed. The features extracted from iris pattern and palmprint pattern are fused by feature-level fusion into a multimodal pattern vector of size 1408 bits. The recognition rate achieved by the LVQ neural network is 94.50%. This system can complete recognition in 15.25 microseconds so that it can be made use for implementing real-time recognition tasks.

Face Recognition Door Unlock System

Face recognition system is widely used for human identification particularly for security functions. The project deals with the look and implementation of secure automatic door unlockby using Raspberry Pi. Web camera for capturing the images from the video frame is operated and controlled by raspberry pi using Open CVPython library to train and store human faces for recognition. In this project we are using Raspberry Pi as face recognition module to capture human images and it will compare with stored data base images. If it matches with authorized user then system allows to supply power to electromagnetic lock to create magnetic field for unlocking the door. The need for facial recognition system that is fast and accurate is continuously increasing which can detect intruders and restricts all unauthorized users from highly secured areas and aids in minimizing human error. Face recognition is one of the most Secured System than the biometric pattern recognition technique which is used in a large spectrum of applications.The time and accuracy factor is considered about the major problem which specifies the performance of automatic face recognition in real time environments. Various solutions have been proposed using multicore systems. By considering present challenge, this provides the complete architectural design and proposes an analysis for a real time face recognition. Thus, the image extracted and allowed to match with the database pictures. If the images are matched, the door unlocks mechanically. the planning of the face recognition system exploitation Raspberry pi will create the smaller, lighter and with lower power consumption, therefore it's a lot of convenient than the PC-based face recognition system. Principle element analysis LBPH (Local Binary Pattern Histogram) algorithmic program is employed for the face recognition and detection method. Then acknowledgement are send through Zigbee module from transmitter to receiver. If image isn't detected in database then it'll ask for manual four digit pin for unlocking the door.The developed theme is affordable, fast, and extremely reliable and provides enough flexibility to suits any environment of various systems. Problem Statement:In theworld of emerging technology, security became an essential component in day to day life. Information theft, lack of security and violation of privacy etc. are the essential components which are needed to be protected. Using smart secure systems for door lock and unlocking became popular nowadays. This is system is being adapted by many countries and first grade countries such as USA, Japan etc., already makes use of this system. This system provides either a facial recognition security feature or a keypad is provided to enter the passcode which unlocks the door. Although, it provides security to the doors, it also has somelimitations and drawbacks: Firstly, if the system mainly uses a facial recognition module, there might be a slight chance that sometimes the face may not be detected and hence the door cannot be unlocked. Secondly, if the system uses a

Optimization of Artificial Neural Networks with Genetic Algorithms for Biometric Pattern Recognition

Abstract The process of pattern recognition in the biometrics is particularly important. The patterns can differ from each other a lot, and even the samples can be significantly different from the templates. The Artificial Neural Networks can be applied as a universal approximator to recognize the patterns with more flexibility. However the topology of the networks determines the processing time and complexity of the hardware of the physical environments. The Genetic Algorithms can be used with success in optimization problems like in this situation, the topology of the neural network is more optimal if we apply the Genetic Algorithms. This study introduce an algorithm in which a tailor made algorithm correcting the topology to enhance the effectiveness of the process.

Biometric pattern recognition using wide networks of gravity proximity measures

In this article a new interpretation of the pattern recognition problem is proposed, in accordance with the problem the own domain of pattern (image) classes are gravitational fields generated by immovable objects (classes of patterns) that propagate in the feature space. A measure of proximity is proposed for calculating the ‘attractive force’ of the pattern to existing templates in the feature space, which can work with both strongly dependent features and with independent ones. A network of gravitational proximity measures was configured to recognize a person by the characteristics of voice and handwritten passwords reproducing. Comparatively low error rates are obtained.

Survey of biometric pattern recognition via machine learning techniques

Survey of biometric pattern recognition via machine learning techniques

Biometric Pattern Recognition from Social Media Aesthetics

Online social media (OSN) has witnessed a significant growth over past decade. Millions of people now share their thoughts, emotions, preferences, opinions and aesthetic information in the form of images, videos, music, texts, blogs and emoticons. Recently, due to existence of person specific traits in media data, researchers started to investigate such traits with the goal of biometric pattern analysis and recognition. Until now, gender recognition from image aesthetics has not been explored in the biometric community. In this paper, the authors present an authentic model for gender recognition, based on the discriminating visual features found in user favorite images. They validate the model on a publicly shared database consisting of 24,000 images provided by 120 Flickr (image based OSN) users. The authors propose the method based on the mixture of experts model to estimate the discriminating hyperplane from 56 dimensional aesthetic feature space. The experts are based on k-nearest neighbor, support vector machine and decision tree methods. To improve the model accuracy, they apply a systematic feature selection using statistical two sampled t-test. Moreover, the authors provide statistical feature analysis with graph visualization to show discriminating behavior between male and female for each feature. The proposed method achieves 77% accuracy in predicting gender, which is 5% better than recently reported results.

Signatures verification based on PNN classifier optimised by PSO algorithm

In this paper, we propose a new biometric pattern recognition method. In classical techniques only features of raw objects are compared. In our approach we will use composed signatures’ features. Features of a signature are associated with appropriate similarity coefficients and individually matched to a given signature. If it is necessary, composed features can be reduced. In the proposed study the most promising results are obtained from Hotelling's approach. Data comprising the composed features allow to achieve higher signature recognition level, compared to unprocessed (raw) data. It is the greatest novelty of the paper—the proposed method of data reduction together with a new type of similarity measure gives a high signature recognition level for various classes of classifiers.Leaning on investigations carried out, the classifier based on the Probabilistic Neural Network (PNN) has been introduced. Optimal parameters of the PNN have been determined by means of the Particle Swarm Optimization (PSO) procedure. The two class PNN classifier demonstrates high efficiency, compared to other classifiers. The described signature verification system consists of three units where features are captured, composed features are prepared, data are reduced and verified. The results of the study carried on signatures of the SVC2004 and MCYT databases and demonstrate the effectiveness of the proposed approach in comparison with other methods from the literature.

Chaotic Neural Network for Biometric Pattern Recognition

Biometric pattern recognition emerged as one of the predominant research directions in modern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.

Biometric Authentication using Human Footprint

The password based authorization scheme for all available security systems can effortlessly be hacked by the hacker or a malicious user. One might not be able to guarantee that the person who is using the password is authentic or not. Only biometric systems are one which make offered automated authentication. There are very exceptional chances of losing the biometric identity, only if the accident of an individual may persists. Footprint based biometric system has been evaluated so far. In this paper a number of approaches of footprint recognition have been deliberated. General Terms Biometric pattern recognition, Image processing.

Face and Speech Recognition Fusion in Personal Identification

Security personnel manning access points most often based their access authorisation on recognition of faces. And it is also very common to base access decision for a person knocking at the door on recognition of his/her voice. The conventional manual method of drawing attention by knocking the door, pressing door bell, or/and shouting one’s presence in order to gain access are inefficient and risky. A better method is automatic personal identification based on face and speech recognition which is the subject of this paper. Eigenface method is used for the face recognition. While the speaker and spoken command recognition are both based on the same mel-frequency cepstral coefficients as feature vectors extracted from English and Yoruba utterances. Experiments yielded 90% face recognition while recognition rates for speakers and the spoken commands were 87% and 74% for utterances in English and Yoruba respectively. However, the final recognition decision for authorisation or access activation is based on the recognition outcomes of the face recognition and the speech recognition. General Terms Biometric Pattern Recognition

Geometric Algebra Neuron for SAR Automation Target Recognition

Biometric Pattern Recognition aim at finding the best coverage of per kind of sample’s distribution in the feature space. This paper employed geometric algebra to determine local continuum (connected) direction and connected path of same kind of target of SAR images of the complex geometrical body in high dimensional space. We researched the property of the GA Neuron of the coverage body in high dimensional space and studied a kind of SAR ATR(SAR automatic target recognition) technique which works with small data amount and result to high recognizing rate. Finally, we verified our algorithm with MSTAR (Moving and Stationary Target Acquisition and Recognition) [1] data set.

Biometric dispersion matcher versus LDA

This paper compares the recently developed biometric dispersion matcher (BDM) with the classical linear discriminant analysis (LDA) for biometric pattern recognition. BDM is extended to the BDM with simultaneously diagonalization (BDMSD) of the covariance matrices and, from a theoretical point of view, it is demonstrated that the feature selection of LDA and BDMSD are equivalent. However, LDA uses the between-class scatter matrix ( S B ) only for feature selection and BDMSD also uses it for classification. This implies a set of advantages. Mainly the BDMSD offers better generalization capability for classifying samples of users that have not been used for training the classifier. Experimental results show that BDM and BDMSD outperform LDA in face recognition and hand-geometry recognition. These two cases correspond to very different situations: number of samples greater than their dimensionality (hand-geometry) and number of samples similar to their dimensionality (face recognition).

Covering framework and primary direction neural network

Some widely used artificial neural networks such as BP, RBF and ARTMAP were discussed on the common points they shared when solving nonlinear regression problems. A framework, proposed accordingly, suggested that such problems should be implemented in two steps: sample covering and model-based map regression. This idea stems from two aspects, i.e. simulation of human brain's information processing and Biometric Pattern Recognition (BPR). As an instance, a new network named as Primary Direction Neural Network (PDNN) was proposed on the ground of the density of certain function set in continuous function space. It is used to solve two kinds of problems, function regression and stochastic time series prediction. Compared with BP and RBF neural network, PDNN has preferable approximation and robust performance.