Fingerprint Recognition System Research Articles

Enhancing Cashless Transactions: The Role and Benefits of Fingerprint Technology

In the modern times cashless transactions have become increasingly prevalent in today's digital economy and strong security measures are important to safeguard all the sensitive data related to finances. Fingerprint recognition emerges as a safe solution to upgrade the security and convenience of existing cashless systems. This research paper explores the utilization and benefits of fingerprint recognition./ The paper begins by understanding the basics of this technology, its advancement and uses. It then dives into the integration of cashless transactions with fingerprint recognition systems highlighting the potential benefits. Moreover, this paper also analyzes real- word scenarios and implementation across various industries and geographies. Furthermore, the technical considerations and challenges associated with implementation are also discussed which includes scalability, accuracy, accessibility of the technology. The strategies to make this technology more efficient and compliant are also discussed. In conclusion, this research accentuates the potential to revolutionize the landscape of cashless transactions using fingerprint recognition technology. By addressing and solving the challenges the full potential of biometric technology to upgrade digital payments can be unlocked.

MoSFPAD: An end-to-end ensemble of MobileNet and Support Vector Classifier for fingerprint presentation attack detection

Automatic fingerprint recognition systems are the most extensively used systems for person authentication although they are vulnerable to Presentation attacks. Artificial artifacts created with the help of various materials are used to deceive these systems causing a threat to the security of fingerprint-based applications. This paper proposes a novel end-to-end model to detect fingerprint Presentation attacks. The proposed model incorporates MobileNet as a feature extractor and a Support Vector Classifier as a classifier to detect presentation attacks in cross-material and cross-sensor paradigms. The feature extractor’s parameters are learned with the loss generated by the support vector classifier. The proposed model eliminates the need for intermediary data preparation procedures, unlike other static hybrid architectures. The performance of the proposed model has been validated on benchmark LivDet 2011, 2013, 2015, 2017, and 2019 databases, and overall accuracy of 98.64%, 99.50%, 97.23%, 95.06%, and 95.20% are achieved on these databases, respectively. The performance of the proposed model is compared with state-of-the-art methods and is able to reduce the average classification error of 3.63%, 1.86%, 1.83%, 0.05%, 0.93% on LivDet 2011, 2013, 2015, 2017, and 2019 databases, respectively for same and cross material protocols in intra-sensor paradigm. The proposed method also reduced the average classification error of 1.59%, 1.41%, and 2.29% for LivDet 2011, 2013, and 2017 databases, respectively for the cross-sensor paradigm. It is evident from the results that the proposed method outperforms state-of-the-art methods in intra-sensor as well as cross-sensor paradigms in terms of average classification error.

Developing and valuation for Techniques Fingerprint Recognition System

الأنماط المميزة التي يلاحظها على أطراف الأصابع الإنسان هي نتيجة لمزيج من حواف وعمق الموجودة على سطح الجلد. تحقق هذه الأنماط تطورها الكامل خلال فترة الحمل وتبقى مستقرة طوال عمر الفرد. تشير بصمات الأصابع إلى الانطباعات التي تشكلها الأنماط الفريدة الموجودة على أطراف الأصابع. يمكن للكدمات الناتجة عن الإصابات العرضية، مثل الجروح والحروق، أن تؤثر بشكل مؤقت على رؤية بصمات الأصابع. ومع ذلك، بعد شفاء الآفة بالكامل، سيتم استعادة الأنماط الأصلية. يعد التعرف على بصمات الأصابع مجالًا ديناميكيًا للغاية ويتم بحثه على نطاق واسع في مجال القياسات الحيوية. تشير المقالة إلى الشكل المختصر للتقنية الحسابية المستخدمة للتأكد من التوافق بين بصمتين بشريتين متميزتين. في هذه المشكلة الصعبة المتعلقة بالتعرف على الأنماط، من الضروري تقليل معدلين للخطأ: معدل القبول الزائف (FAR) ومعدل الرفض الزائف (FRR). معدلات الخطأ هذه تتنافس مع بعضها البعض. لقد سهّل تطوير القدرات الحاسوبية إنشاء أنظمة التحقق من بصمات الأصابع (AFIS)، مما أدى إلى زيادة كبيرة في النشاط البحثي، خاصتا في العشرين عامًا الماضية. تهدف هذه المقالة إلى تقديم مراجعة شاملة للمشكلة المحيطة بالتعرف على بصمات الأصابع، بما في ذلك تحليل التصميم الأساسي وصعوبات التنفيذ، إلى جانب تقييم التطورات المستقبلية المحتملة.

Unlocking Security: The Significant Role of Machine Learning and Deep Learning Techniques in Fingerprint Recognition Systems

One of the more widely utilized biometrics for in-person identification is a fingerprint. It has been discovered that no two people have the same fingerprints and that each one is distinct. A person's fingerprint traits remain constant as they age. Compared to DNA, fingerprints are more distinctive. Identical twins cannot have the same fingerprints even though they have the same DNA. Artificial Intelligence encompasses the broader goal of creating intelligent systems, while Machine Learning focuses on developing algorithms that enable machines to learn from data. Deep Learning, as a subset of Machine Learning, leverages neural networks with multiple layers to learn complex representations of data. While Deep Learning has become increasingly prominent and successful in recent years, it is just one of the many approaches within the broader field of AI and Machine Learning. Fingerprint recognition stands as one of the oldest and most widely used biometric authentication methods, finding applications across diverse domains such as law enforcement, access control, and mobile device security. With the advent of machine learning and deep learning techniques, significant strides have been made in enhancing the accuracy, efficiency, and scalability of fingerprint recognition systems. This comprehensive review aims to provide a thorough examination of the pivotal role played by machine learning and deep learning methodologies in advancing fingerprint recognition technology. The paper commences with an introduction to the importance and ubiquity of fingerprint recognition, elucidating its significance in various real-world applications. Subsequently, it delves into the foundational concepts of machine learning and deep learning, elucidating their relevance to fingerprint recognition tasks. Key machine learning algorithms such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Random Forests, and neural network-based approaches are discussed, highlighting their strengths and limitations in the context of fingerprint recognition.

Toward Synthetic Physical Fingerprint Targets.

Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.

Innovative Solutions for Worn Fingerprints: A Comparative Analysis of Traditional Fingerprint Impression and 3D Printing.

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, which results in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking with worn fingerprints. The research findings indicated that the traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, the traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D-printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on the unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.

SMART SENSOR BASED TOUCHLESS FINGERPRINT SYSTEM

his paper introduces a touchless fingerprint recognition system, offering a hygienic alternative to conventional touch-based systems. The primary objective is to develop and evaluate a comprehensive solution that addresses hygiene-related concerns and mitigates the risks associated with disease transmission, particularly in the context of the ongoing Covid-19 pandemic. The proposed system utilizes a state-of-the-art touchless sensor to capture intricate fingerprint images, subsequently processing, storing, and retrieving them for matching purposes. By eliminating the need for physical contact, the system effectively tackles issues related to inconsistent pressure and prolonged collection times. The touchless fingerprint technology showcased in this system holds great promise for diverse applications that demand secure and hygienic fingerprint recognition. The proposed method overcomes the limitations inherent in touch-based systems, making it well-suited for environments where health and safety considerations are paramount. The integration of this touchless technology not only enhances security but also contributes significantly to public health by reducing the risk of cross-contamination in shared touchpoints. As the world navigates the challenges posed by the pandemic, the adoption of touchless fingerprint recognition systems represents a crucial step toward creating safer and more hygienic environments.

IOT BASED ATTENDANCE SYSTEM USING FINGERPRINT SENSOR

To provide reliable, time-saving and automatic class attendance system, the concept of Internet of Things (IoT) based class attendance monitoring system using embedded Linux platform is presented in this project . The study is focused on the design and implementation of fingerprint detection and recognition system using Arduino Uno. The system takes fingerprint of students, and analyzes, detects and recognizes fingerprint using sensor .After collecting processing data, the system generates a final attendance record and uploads it in a cloud server. The cloud server has been implemented using python based web framework. The record can be accessed remotely from a user-friendly, web application using the Internet. Finally, the system is also capable of sending an email notification with the final record to the teachers and students in a specific time. Tests and performance analysis were done to verify the efficiency of this system

Artificial multisensory system with optical feedback for multimodal perceptual imaging

Artificial sensory systems require appropriate input–output modalities to enhance interaction efficiency and accuracy. It is crucial to mimic an organism to perceive and integrate multiple external stimuli and to construct systems with multiple sensory inputs. In addition, the application of optical output to the sensory system not only solves the problem of electrical signal transmission, but also enables direct imaging through optical output. Here, we propose an artificial multisensory system with optical feedback (AMSOF), which can simultaneously receive pressure and thermal stimuli and then generate neuromorphic optical responses along with feedback. Among them, the triboelectric nanogenerator (TENG) is responsible for receiving pressure stimuli, while the artificial light-emitting synaptic device utilizes the quantum dot thermal quenching mechanism to processes temperature signals and generate optical feedback. Additionally, if the AMOFS is applied to the fingerprint recognition system, it can enable fingerprint imaging through dual recognition of these two signals. In other words, it can enhance the security of biometric authentication by adding the capability to distinguish the live nature of objects on top of traditional fingerprint recognition. Therefore, this system aims to achieve more intelligent and efficient human–computer interaction and environmental perception capabilities, bringing forth a multitude of applications and innovations for smart systems.

Advancing Fingerprint Template Generation and Matching With Recast Minutiae Clustering and mRBFN

Rapid development of automation in the day-to-day life activity marks up the need of securing bio-metric template and the privacy of rightful owner. Minutiae-based matching is the most popular in the fingerprint recognition system, which greatly suffers from non-linear distortion like translation and rotation. To deal with linear distortion most of the technique proposed in the literature depends upon a reference or singular point. The paper proposes a binary template generation technique which applies an unsupervised clustering technique without fixing the number of clusters. Instead of position and orientation of the minutiae points the cardinality of the clusters are stored and converted into binary template. No spatial pattern information about the fingerprint is stored in the template to protect it from spoofing and information leakage. By the help of modified Radial Basis Function Network (mRBFN) with robust and efficient matching technique the generated templates are matched for authentication. We use MCYT dataset for training the mRBFN. The efficiency of the proposed scheme is evaluated on FVC 2000, FVC 2002 and FVC 2004 dataset.

People identification via tongue print using fine-tuning deep learning

<span>Many person-verification systems are critical in security systems for verifying passage through doors opened to specific people using various techniques. People can use electronic payment methods and security apps to generate codes for quick, remote financial transactions. Older systems required precision and speed. Many alternative methods were developed by technology and artificial intelligence to make such operations simple and quick. The identification of tongue prints is discussed in this paper. Tongue prints, like fingerprints, are unique to each individual. The tongue was used in this study because it is unique among such organs. The tongue is protected by the lips. This guards against taking a tongue print by force. Some people distort their fingerprints, making fingerprint recognition systems unable to recognize them. Car accidents cause facial distortion, which distorts the system and prevents it from distinguishing facial prints, so the tongue was used as a fingerprint in this study. A database of 1,104 images for 138 Mustansiriyah University College of Science students yielded an average of eight images per individual. VGG16 was implemented for transfer learning and fine-tuning. In comparison to previous studies, the accuracy achieved was more than 91%.</span>

A uniform representation model for OCT-based fingerprint presentation attack detection and reconstruction

In current Optical Coherence Tomography (OCT)-based fingerprint recognition systems, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction are treated as two independent branches, resulting in high computation and system building complexity. Therefore, this paper proposes a uniform representation model for simultaneous PAD and subsurface fingerprint reconstruction. A novel semantic segmentation network using attention mechanisms was designed to extract and segment multiple subsurface structures from real finger slices (i.e., B-scans). The latent codes derived from the network are directly used to effectively detect PA because they contain abundant subsurface biological information, which is independent of PA materials and has strong robustness for unknown PAs. Segmented subsurface structures were adopted to reconstruct multiple subsurface 2D fingerprints. Extensive experiments were carried out on an in-house database, which is the largest public OCT-based fingerprint database with 2449 volumes. PAD performance was evaluated by comparing the results of the existing methods with those of other segmentation networks. The proposed uniform representation model can obtain an accuracy (Acc) of 96.63%, which achieves a state-of-the-art performance. The effectiveness of subsurface reconstruction was evaluated from the segmentation and recognition results. In the segmentation experiments, the proposed method achieved the best results with an Intersection of Union (mIOU) of 0.834 and a Pixel Accuracy of 0.937. By comparing the recognition performance on surface 2D fingerprints (e.g., commercial and high-resolution), the lowest results with an Equal Error Rate (EER) of 2.25% by minutiae matching and an EER of 5.42% by pore matching are achieved, which indicates the excellent reconstruction capability of the proposed uniform representation model.

An Image Unmixing and Stitching Deep Learning Algorithm for In-Screen Fingerprint Recognition Application

The market share of organic light-emitting diode (OLED) screens in consumer electronics has grown rapidly in recent years. In order to increase the screen-to-body ratio of OLED phones, under-screen or in-screen fingerprint recognition is a must-have option. Current commercial hardware schemes include adhesive, ultrasonic, and under-screen optical ones. No mature in-screen solution has been proposed. In this work, we designed and manufactured an OLED panel with an in-screen fingerprint recognition system for the first time, by integrating an active sensor array into the OLED panel. The sensor and display module share the same set of fabrication processes when manufactured. Compared with the current widely commercially available under-screen schemes, the proposed in-screen solution can achieve a much larger functional area, better flexibility, and smaller thickness, while significantly reducing module cost. A point light source scheme, implemented by lighting up a single or several adjacent OLED pixels, instead of a conventional area source scheme as in the CMOS image sensor, or a CIS-based solution, has to be adopted since the optical distance is not long enough due to the integration. We designed a pattern for the point light sources and developed an optical unmixing network model to realize the unmixing and stitching of images obtained by each point light source at the same exposure time. After training, data verification of this network model shows that this deep learning algorithm outputs a stitched image of large area and high quality, where FRR = 0.7% given FAR = 1:50 k. In despite of a poorer quality of raw images and a much more complex algorithm compared with current commercial solutions, the proposed algorithm still obtains results comparable to peer studies, proving the effectiveness of our algorithm. Thus, the time required for fingerprint capture in our in-screen scheme is greatly reduced, by which one of the main obstacles for commercial application is overcome.

Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms

The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success.

LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer.

With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%.

A novel intelligent 12-layer convolutional neural network model for gender classification using fingerprint images

Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%.

Perancangan Aplikasi Pengolahan Citra Digital Untuk Penajaman Sisi Citra Hasil Fingerprint Menggunakan Metode Fourier Phase Only Synthesis

Detecting fringerprints (fingerprints) is one way to sharpen image results to make them look clearer and cannot be manipulated. In general, the attendance system used by employees is still mostly done manually. This has many weaknesses where in addition to data that can be easily imitated, the time is not clear, it also takes quite a long time. So to avoid this, an electronic system is needed. In this case the system that will be used is the fingerprint method. The development of image processing technology now provides the possibility for humans to create a system that can recognize a digital image. Various image processing applications that have been developed at this time include security systems, the world of robotics, and so on. Image processing is one type of technology to solve problems regarding image processing. In image processing, images are processed in such a way that they can be used for further applications. The fingerprint recognition system must be able to identify a person's fingerprint from a large set of fingerprint databases. This is a separate problem for the efficiency of the identification system. Therefore, various classification approaches are used based on the general characteristics that appear in fingerprints. The process begins with detecting the fringerprint result by sticking your finger on the scan machine, then the image will be detected and the image is sharpened with a value using arithmetic operations. The results of image processing are indicated by changes in the resulting image and fourier image changes. In this case the system that will be used is the fingerprint method. This fingerprint system uses the fourier phase only synthesis method.

An Intelligent System for Fingerprint Recognition and Verification Using Dilated Convolutional Neural Network and Extreme Learning Machine

Several applications currently utilize fingerprint-based user recognition and authentication, but obtaining complete accuracy (eliminating false matches) is still challenging. Prior to feature extraction, improper image alignment was one of the causes of this problem. This paper proposes a novel fingerprint recognition and verification system using a dilated convolutional neural network (DICNN) and a weighted and bias-optimized extreme learning machine (WBELM). Image preprocessing, feature extraction, and fingerprint recognition are the main phases of the proposed work. To begin, image preprocessing is performed using bilateral filtering to suppress the noise in the gathered images. After that, the proposed system uses DICNN to extract features from the preprocessed images. From the extracted features, fingerprint recognition is done using WBELM. This study conducted experiments on the publicly available FVC 2004 dataset. The proposed model achieves 98.54 percent recognition accuracy with an elapsed time of 584 ms.

Convolutional neural network based children recognition system using contactless fingerprints.

Biometric features are useful for unique identification, authentication, and security applications. Among all biometric features, fingerprints are the most commonly used because they contain ridges and valleys. There are challenges in recognizing child or infant fingerprints since the ridges are not mature as the hands are covered with a white substance and acquisition of fingerprint images is difficult. In the context of COVID-19 pandemic, contactless fingerprint acquisition gains importance as it is not infectious especially with children. In this study, a Convolutional Neural Network (CNN) based children recognition system named Child-CLEF, that uses Contact-Less Children Fingerprint (CLCF) dataset acquired using a mobile phone-based scanner is proposed. The quality of captured fingerprint images is enhanced using a hybrid image enhancement method. Furthermore, the minutiae features are extracted using the proposed Child-CLEF Net model and the identification of children is made using a matching algorithm. The proposed system is tested with a self-captured children fingerprint dataset, CLCF and publicly available PolyU fingerprint dataset. It is found that the proposed system outperforms the existing fingerprint recognition systems in terms of accuracy and equal error rate.

Intelligent and assisted medicine dispensing machine for elderly visual impaired people with deep neural network fingerprint authentication system

Offering a medication to Elderly and Visually Impaired People (EVIP) in rural areas without staff assistance is a difficult task in the field of health care. The current pill dispenser has limitations when it comes to administering medication before and after food, doesn't take into account over- and under-dosing situations, and doesn't send notifications to the relevant medical entities when the weight of the pills falls below the threshold. Despite the availability of numerous techniques and methods for automating the delivery of pills, there is no assistive method for the timely and secure delivery of medication. The suggested architecture provides EVIP with five crucial capabilities that aren't present in any of the current pill dispensers in an integrated way. In this paper, we present a biometric sensor-based secure access method for medication kits. The right person is only allowed access to the kit based on the finger print scanner. Second, the suggested architecture provides a self-managed and patient-centric service through assisted and automatic medication delivery. The third feature of this paper is the design method for the efficient delivery of pills before and after meals. A method of alerting the medical entities whenever a medication is over or under dosed is a fourth feature of the system. As a fifth feature, the implementation also automatically notifies the pharmacist and doctor when the weight of the pill strips in the chamber falls below the threshold value. Through the use of an integrated Timely Assistance Messaging (TAM) algorithm, all five features are implemented in the suggested architecture. A finger print recognition system is included with VGG-16 deep neural network with image enhancement. The EVIP can use this proposed system in residences, medical facilities, and retirement communities.