Convolutional Neural Network (CNN)-Based Signature Verification via Cloud-Enabled Raspberry Pi System

This Raspberry Pi Single Board Computer-Based Cataract Detection System using Deep Convolutional Neural Network through GoogLeNet Transfer Learning and MATLAB digital image processing paradigm based on Lens Opacities Classification System III with Python application, which would capture the image of the eyes of cataract patients to detect the type of cataract without using dilating drops. Additionally, the system could also determine the severity, grade, color or area, and hardness of cataract. It would also display, save, search and print the partial diagnosis that can be done to the patients. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models was used as the methodologies of this study. Cataract patients and ophthalmologists of one of the eye clinics in City of Biñan, Laguna, as well as engineers and information technology professionals tested the system and also served as respondents to the conducted survey. Obtained results indicated that the detection of cataract and its characteristics using the system were accurate and reliable, which has a significant difference from the current eye examination for cataract. Generally, this would be a modern cataract detection system for all Cataract patients

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Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a1018.1191s19 ◽

2019 ◽

Vol 9 (1S) ◽

pp. 76-80

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Learning Ability ◽

Signature Verification ◽

Support Vector ◽

Svm Classifier ◽

Powerful Learning ◽

Offline Signature Verification ◽

Learning Technique ◽

Handwritten Signature

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.

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Autentikasi Daun Herbal Menggunakan Convolutional Neural Network dan Raspberry Pi

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI) ◽

10.22146/.v9i3.302 ◽

2020 ◽

Vol 9 (3) ◽

pp. 278-286

Author(s):

Haryono ◽

Khairul Anam ◽

Azmi Saleh

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Raspberry Pi ◽

Testing Data

Saat ini, metode autentikasi daun banyak digunakan dalam proses klasifikasi tanaman herbal. Pada dasarnya, metode autentikasi daun merupakan perbandingan antara gambar yang akan diidentifikasi dengan gambar referensi yang dibuat dalam dataset. Tujuan makalah ini adalah mengidentifikasi daun tanaman herbal menggunakan metode kecerdasan buatan, yaitu Convolutional Neural Network (CNN) yang ditanam pada Raspberry Pi. CNN memiliki keunggulan yaitu tidak memerlukan feature extraction karena di dalam CNN sudah terdapat feature extraction otomatis. Makalah ini menggunakan tujuh jenis daun dari tanaman herbal yang berbeda. Gambar daun diambil menggunakan kamera dan diproses oleh Raspberry Pi yang diintegrasikan dengan CNN. Identifikasi dilakukan pada tujuh jenis tanaman obat yang dibagi menjadi dua pertiga data training dan sepertiga data testing. Hasil dari proses identifikasi divalidasi dengan data lain yang tidak termasuk dalam data training dan data testing, serta data daun selain dari tujuh jenis daun yang diidentifikasi. Metode CNN menunjukkan hasil yang bagus dalam proses autentikasi dengan tingkat akurasi 93,62% untuk testing data secara offline dan 91,04 % untuk testing data secara online.

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Sistem Pengenalan Benih Padi menggunakan Metode Light Convolutional Neural Network pada Raspberry PI 4 B

Jurnal Teknologi Terpadu ◽

10.54914/jtt.v7i2.443 ◽

2021 ◽

Vol 7 (2) ◽

pp. 120-126

Author(s):

Indra Hermawan ◽

Defiana Arnaldy ◽

Maria Agustin ◽

M. Farishanif Widyono ◽

David Nathanael ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Personal Computer ◽

Convolution Neural Network ◽

Raspberry Pi

Baru-baru ini, metode pembelajaran mendalam dengan Convolution Neural Network (CNN) telah banyak digunakan untuk tugas klasifikasi gambar. CNN memiliki keunggulan yang tak tertandingi dalam mengekstraksi fitur gambar diskriminatif. Namun, banyak metode berbasis CNN yang ada dirancang untuk lebih dalam dan lebih besar dengan lapisan yang lebih kompleks. Sehingga membuatnya sulit untuk diterapkan pada perangkat seluler atau pada perangkat waktu nyata yang menggunakan mikrokontroler seperti raspberry pi, Arduino, dan lain sebagainya. Hal tersebut diatasi dengan menggunakan Light Convolution Neural Network (LCNN), maka perlu dilakukan percobaan untuk menguji seberapa besar perbedaan kinerja LCNN pada Personal Computer (PC) dan pada mikrokontroler raspberry pi 4 dengan sistem operasi Raspbian. Eksperimen dilakukan dengan menggunakan beberapa parameter kinerja yaitu accuracy, F-1 Score, recall, precision, dan waktu dari pengujian klasifikasi untuk mendapatkan hasil performa dari pembelajaran mendalam. Oleh karena itu, hasil dan arsitektur model akan mengkonfirmasi perbedaan kinerja di masing-masing perangkat dan menunjukkan bagaimana performa model pada perangkat yang dibatasi sumber daya atau berjalan secara waktu nyata. Pengujian menunjukkan bahwa kinerja pada raspberry pi yang merupakan alat dengan sumber daya terbatas tidak mempengaruhi kualitas pengenalan gambar, tetapi mempengaruhi waktu pemrosesan pengenalan, dikarenakan raspberry pi membutuhkan waktu proses yang lebih lama untuk melakukan satu proses pengenalan data atau foto. Hal tersebut akan mengakumulasi waktu yang dibutuhkan untuk pemrosesan data yang banyak, sehingga dapat disimpulkan bahwa raspberry pi dan alat dengan sumber daya terbatas sangat tidak efektif untuk melakukan pelatihan pengenalan dan melakukan proses pengenalan yang berisi banyak data atau foto dalam sekali prosesnya.

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Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37622 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1573-1582

Author(s):

S Gopi Naik

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Optimization Algorithms ◽

Object Identification ◽

Integrated System ◽

Raspberry Pi ◽

High Level

Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.

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Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

Applied Sciences ◽

10.3390/app10217448 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7448

Author(s):

Jorge Felipe Gaviria ◽

Alejandra Escalante-Perez ◽

Juan Camilo Castiblanco ◽

Nicolas Vergara ◽

Valentina Parra-Garces ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real Time ◽

Urban Areas ◽

Low Cost ◽

Raspberry Pi ◽

Automatic Identification ◽

Signal Recognition ◽

Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

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3-D Offline Signature Verification with Convolutional Neural Network

10.5121/csit.2020.101518 ◽

2020 ◽

Author(s):

Na Tyrer ◽

Fan Yang ◽

Gary C. Barber ◽

Guangzhi Qu ◽

Bo Pang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Recognition ◽

Signature Verification ◽

Offline Signature Verification ◽

Optical Profilometer ◽

Novel Method

Signature verification is essential to prevent the forgery of documents in financial, commercial, and legal settings. There are many researchers have focused on this topic, however, utilizing the 3-D information presented by a signature using a 3D optical profilometer is a relatively new idea, and the convolutional neural network is a powerful tool for image recognition. The present research focused on using the 3 dimensions of offline signatures in combination with a convolutional neural network to verify signatures. It was found that the accuracy of the data for offline signature verification was over 90%, which shows promise for this method as a novel method in signature verification.

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Multi-Model Biometric Authentication System for Smart Attendance

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9094.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4284-4287

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Raspberry Pi ◽

Machine Learning Algorithm ◽

Biometric Authentication ◽

Biometric System ◽

Authentication System ◽

Paper Method

To increase the success rate in academics, attendance is an essential aspect for every student in schools and degree colleges. In olden days, this attendance is manually taken by teachers with pen and paper method, which consumes more amount of time in their busy management scheduling era. To make this attendance taking more comfortable and more accurate, a multi model biometric system for attendance monitoring system is proposed using a Raspberry Pi single-board computer. The camera and biometric device which is connected to the system gathers Information regarding the students by recognizing their faces and their fingerprint simultaneously. If both of them match with the student details stored in the database, then the system will be sending an alert about the student presence in the class. The student details which is stored into the database is collected from the students initially. By using these details like images and fingerprints the system is trained by using a Convolutional Neural Network (CNN) Machine Learning Algorithm.

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