Smart Attendance Capturing Using Mobile App

A Smart and Secure Attendance capturing mobile app which capture basic details of employees like Photograph, Name, Designation, Employee Number, Gender, Office Address, etc.

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Inspiration

Currently our life style is classified as before covid-19 and after covid-19. This project mainly focus on after covid-19. Before covid-19 , Biometrics like fingerprint Sensor is used for security purpose and attendance, but after covid-19 directly contacting with these type of security devices is not recommended in public places. Hence this project is designed on high level security purpose.

What it does

Our system is Smart and secure Attendance Capturing Mobile Application , Which captures employee photo with other details such as Name, Employee id, Gender, Office Address and other details.

How we built it

Application is built using a App Developing IDE Known as Android Studio , XML is used to design UI Layouts and Java is used as the Backend language for the application and mainly Tensor Flow is used to classify the images. A trained model is used to classify the images in the application. Model is imported inside the application and the data's are store in Firebase database.

Challenges we ran into

The main Challenging task was to built Machine Learning. Image classification is very vast domain, that was the main challenge. Classifying the Image probability was very low , so after identifying that, we increased the number of images for classification.

Accomplishments that we're proud of

Biometrics such as Fingerprints are used in the public places due to covid-19 situation contact with the devices are not recommended ,Hence the application will pay the way for security and mainly used as Attendance Monitoring System

What we learned

Here we learned to built an ML model with Tensor flow using Python Programming Language we also learnt to built application Using Java and XML and also learnt to bind the ML model with my application.

What's next for Smart Attendance Capturing Using Mobile App

To develop the open CV tensor flow and keras libraries to perform, facial recognition and liveness detection followed by implementing dynamically run source code to create datasets upon which the model was trained

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