Authentication is a significant issue in system control in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications, such as video monitor system, human-computer interaction, and door control system and network security. This project describes a method for Student’s Attendance System which will integrate with the face recognition technology using deep learning algorithms. The system will recognize the students present in the classroom and provide the list of present students for the lecture. The primary technique used for the face detection is by using python inbuilt packages of OpenCV. Once the model is trained on different kinds of datasets, the project will help in identifying students present for the class. The front end will be based on an android application. The application uses SQLite database for establishing connection between web app and the model. The backend model mainly comprises of a convolutional neural network which extracts features and trains the model in recognizing those features. The inbuilt OpenCV uses haarcascade classifiers in identifying the faces present in the input image. The list of identified will be displayed as the end result.

I'm a Java Desktop Application & Native Android Software Developer with 4+ years of experience in full SDLC creating dynamic web applications using a variety of modern technologies. Updates functionality based on customer requirements to ensure an excellent user experience. I have a lot of projects, some of this project are : Mobile application for attendance management using Java. Control a robot using Arduino using c (Mini Project). Stock management software using Java and Netbeans as IDE. Mobile application for counting people in the enterprise using the technology of face recognition and face detection using the technology of JNI java language, C++(OpenCV Library). My other professional skills include: Achieved a 30% reduction in the app's memory consumption by eliminating duplicate strings via Garbage Collection Logs analysis and refactoring the code Recognized and improved code inefficiencies that led to a 40% increase in app stability. Unit Test (Java). Spring Boot Technologie As Back-End Development. UX and UI Designing. MongoDB, FireBase for NoSQL database, SQL Server(Transact SQL), MySQL and OracleDB(PLSQL) JDBC Technologie in Java JavaFX and Swing for Front-End IDE(Eclipse, NetBeans, IntelliJ and Android Studio) Extensive knowledge of C/C++,PHP, Javascript, HTML, CSS, and VB.NET Framework JNI (Java Native Interface) to call native applications and library written in C/C++.

Face Detection Web App - React Front-end

Moto is coming up once again with a device that might feature something more at a mid-range smartphone price. But, does it really fulfil what it needs to do for the users? We are about to find out through this article where the Moto M specifications will be discussed one by one to let you know what we think about this upcoming device. Will it truly fulfil its work by giving us a powerful device, or will it be just one of those phones that pack in features by copying from other high-end brands? Design and Display: When it comes to the body of the Moto M, it might look similar to many smartphones coming up these day. Full metal body, with finger print sensor on the back. This common design may either impress you or may not. But, you do get a robust body. Moreover, it comes in three colour variants, namely – gold, silver and grey. The device also has a Dolby Atmos sound enhancement speaker on the side and active noise cancellation mic as for better recording as well. A 3.5 mm jack is also provided on the body. If we talk about its display, then the Moto M features a 5.5-inch Super AMOLED capacitive touchscreen display with 16 M colours. The 163g body features 401 ppi pixel density with 1080x1920 pixels. Using the device, we found that its multi touch features on the screen are quite good and the user is rarely going to find any lags. The company made sure that they offer a larger screen for the audience so that they may play their favourite games or watch media files with an immersive experience. Performance: Moto’s devices are usually either mid-ranged or high-ranged. This mid-ranged device comprises of a Qualcomm MSM8952 Snapdragon 617 chipset that is good for running high-end games and hi-resolution videos. An, Octa-core processing power comprising of 4 1.5 GHz Cortex-A53 and 4 1.0 GHz Cortex-A53 are added to ensure that multitasking is smooth on the device. An Adreno 405 graphics chipset is also added to render the best of graphics. Software: Moto M offers Android v6.0.1 (Marshmallow) which is not the newest but still is able to run the latest of applications and games. It also offers updates for the OS, and we might expect an update for this device in the future as well. UI of the device is standard Android one with a little bit of Moto’s own features and apps. Camera and Storage: Moto M sports a 16 MP rear camera with f/2.0 aperture and phase detection autofocus feature. This great camera is supported with dual LED flash as well. A number of features are available in the camera settings such as geo-tagging, face detection, touch focus, panorama, auto HDR. The pixel size of the snapped images is 16 µm. An 8 MP front camera is also provided which is decent enough to click selfies. This front camera offers a sensor size of ¼” and can snap at 1.12 µm pixel size. The maximum recording quality of the primary camera is 1080@30 fps. Those who are looking for 4K video shooting get ready to be disappointed as this device does not support 4K. Storage options in Moto smartphones are not a problem. You not only get a great device with decent internal storage of 32 GB and 64 GB but also get an expandable option of up to 256 GB. But, this expandable slot will have to be compromised with the secondary sim slot present in the mobile. The 32 GB version comes with 3 GB of RAM. The 64 GB variant comes with 4 GB of RAM. Battery and Connectivity: Moto is known for its good quality battery performance. And, with a non-removable 3050 mAh battery in the device, it could be said that the Moto M is favourable with its battery life. The addition of a Super AMOLED capacitive touch display ensured that the device uses less power for its bright and colourful screen. Testing on the use of its power has been done, and we can say that it covered up the full day on one complete charge. However, users may have to charge it by the end of the day. Connectivity options on the device comprise of GPS with A-GPS, BDS, GLONASS, Bluetooth v4.1, LE, A2DP, Wi-Fi Direct, Wi-Fi 802.11 a/b/g/n/ac, Mobile hotspot, dual-band, USB On-The-Go and USB Type-C 1.0 reversible connector. The list of sensors on the device are – gyro, compass, accelerometer, fingerprint, and proximity. The Verdict: It might be a little hard to tell about the performance and features of the device. You do find most of these features in many devices present today. But, it does offer them at a decent price. The expected Moto M price in India is Rs. 19,990. Looking at these features, we can say that they are quite good, and people can expect a decent battery life performance as well. But, will it stick to the performance efficiency? We will soon find out after the device hits the stores.

No description available

This web app is designed to detect faces using a machine learning AI called Clarifai. It will detect face and show a bounding box in the face. It is developed using Java script and React on the front end and NodeJS, ExpressJS for back end and it IS RESTFULL API based. The database is Postgres with Knex SQL queries.

Front end API integrated React app for face detection

front end of my face detection app, ready to deploy

Face Recognition web app with Clarifai API, user authentication, React front-end, Node.js/Express back-end, and PostgreSQL for image face detection.

Front end code for a full-stack face detection app built using React, Node.js, Express and PostgreSQL

Face detection React app (front-end files) for images containing a single face. Simply copy an image URL into the search field, and click detect!

A face detection web app with login and register functionality. Front end portion of the final project for ZTM Udemy Course.

A simple Node.js/Express backend that provides user authentication and image recognition functionality using the Clarifai API, with PostgreSQL for persistence. This service is deployed on Railway infra platform and to serve as the backend for a front-end face-detection app.

Face-Detection-App front end

Front end face detection app

Face Detection App (FRONT END)

face-detection app front end

No description available

We can register ourselves and do the face detections by providing the link of some image from the web.

Front End for face-detection app

front end of face detection app

Front end of Face Detection App

front end app for face detection

Face detection web app (front end)

Front end project for face detection app

front end for Face_Detection web app

Face detection app for detecting faces from images - front end

The front end for the face-detection app.

Front-end of Smart-Brain Face Detection App

The front end of my face detection app.