Face Recognition System to RPA Software Design and Implementation

Kevin, Kevin (2020) Face Recognition System to RPA Software Design and Implementation. Project Report. Universitas Internasional Batam. (Submitted)

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Abstract

Proyek ini dilaksanakan dengan tujuan untuk membangun sebuah sistem pengenalan wajah dan diimplementasikan ke sebuah RPA (Robotic Process Automation) software milik Glee Trees Pte. Ltd. yang bernama Gleematic. Sistem pengenalan wajah ini akan digunakan sebagai titik jual, dan fitur baru untuk memperluas kemampuan otomasi Gleematic. Pengembangan project ini berjalan dari tanggal 28 Januari 2019, sampai dengan tanggal 1 Maret 2019. Sistem pengenalan wajah ini dikembangkan menggunakan bahasa pemograman Python. Sisi server dari sistem menggunakan framework Odoo. Algoritma pengenalan wajah yang digunakan adalah sebuah open-source library yang sudah dilatih dan dikembangkan sebelumnya serta sebuah library bernama OpenCV. Sisi client sistem berbasis desktop, dan memerlukan koneksi internet yang stabil. Algoritma pengenalan wajah ini memiliki akurasi ~98% tetapi terbatas dengan rotasi wajah sekitar 30 derajat. Pengguna sistem ini dapat mendaftarkan wajah ke dalam sistem, dan mendeteksi dan mengekstrak informasi dari wajah dengan hanya menggunakan gambar wajah. Sistem ini juga mampu mendaftarkan secara otomatis setiap wajah baru yang dihadapinya. Setelah implementasi, sistem ini sudah dipasarkan ke calon klien sebagai titik jual unik Gleematic. ********************************************************************** The purpose of this project is to build a face recognition system, and implement it into an RPA (Robotic Process Automation) software owned by Glee Trees Pte. Ltd called Gleematic. The face recognition system serves as an additional selling point, and feature for Gleematic to expand its automation capabilities. Development of the project started on the 28th of January 2019 andended on the 1st of March 2019. The system is built using the Python programming language. The server side of the system uses Odoo as the framework. The face recognition algorithm that is used is an open-source library that has been pre-trained and developed beforehand along with a library called OpenCV. The client side of the system is desktop based, and requires a stable internet connection. The face recognition algorithm is able to detect faces with a ~98% accuracy, but is limited to around 30 degrees of rotation of the face. Users of the system are able to register faces into the system, and then later detect and extract information from them using only images of faces through Gleematic. The system is also capable of automatically registering any new faces that it encounters. Since deployment, the face recognition system has been marketed towards potential clients as a unique selling point.

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