International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
Virtual Contact Discovery using Facial Recognition Aryan Thakur1, Sharon Mary Thomas2, Kshitij Tapre3, Vijay Jumb4 1-3 Student,
Dept. of Computer Engineering, Xavier Institute of Engineering, Mumbai, India Dept. of Computer Engineering, Xavier Institute of Engineering, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.2 Scope Abstract - We are surrounded by data. We produce a 4 Professor,
significant amount of data ourselves too. One major challenge we face today is storing and handling this data. Even though we have abstractions such as metadata to help us out, current components that are employed give us a lot to desire for. We are working in this niche to implement a metadata retrieval system using a handy component - our face. We are implementing an example of contact discovery using our facial encodings as the identifying component. We are relying on some Machine Learning models in our stack, so we will also be working on setting up the machine learning pipeline efficiently to give us fast and accurate results.
The applications and the scope of the project would include but is not limited to: i) This can be implemented in a B2B or B2C environment to provide additional features to the end-user. ii) In forensic science to identify a suspect. iii) Face-scanning and verification can be used by companies employing drivers. A selfie could be a mode of authentication to verify the identity of the driver and the passenger before the journey begins and also to give access to the driver to operate the vehicle assigned. Some companies in this domain are currently testing this concept in their trial phase.
Key Words: Contact Discovery, Facial Recognition, DeepFace, Face encodings, Machine Learning
1. INTRODUCTION
iv) To implement a cashless system where the face is used as the identifying element
Carrying meta-data in real life is dependent on using an auxiliary component (url links, systems, rfid/nfc tags etc). The aim of this project is to come up with a solution to use facial as an auxiliary component and associate some form of metadata with it (contact removing the need to carry another physical component and having a better user experience.
2. LITERATURE SURVEY In order to authenticate users through ID verification services, facial recognition systems are often used to match a human face from a digital image or video frame against a database of faces. These systems operate by identifying and quantifying facial features from an image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology. There are APIs already in place in the domain of facial recognition and detection like Kairos (Finding faces and features), Animetrics (Deep-learning powered face recognition ), etc. Our Goal through this project is to optimize and deploy so the latency and accuracy are satisfactory in a resource-restricted environment.
1.1 Objectives The objectives are but not limited to: i) Although the project has its main components as facial detection and verification, the main goal of the project is to find, explore and if possible to set guidelines and ways to optimize the deployment of machine learning models as this particular area has lesser resources to explore as compared to the minor but important phases of this project that are facial detection and verification.
A. Intelligent detection and recognition system for mask-wearing based on improved RetinaFace algorithm [7]: In this paper, Bin Xue et al. have designed a smart detection and system for maskwearing. The system is mainly composed of face mask detection and face recognition algorithm. The main functions of the system can be divided into parts: face mask detection, face recognition, and voice prompts. The paper implements
ii) Optimize the backend logic for real-time performance and accuracy.
iii) Create a user friendly and intuitive flow in the frontend.
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