AI Face Recognition

Creating an innovative platform for emotion recognition, which analyzes multimedia content

ARTIFICIAL INTELLIGENCEEDUCATION, ENTERTAINMENT AND MEDIA
artificial intelligance face recognititon image

Project Concept

The client, approached us with a request to develop this project. The concept of this project involves creating an innovative platform for emotion recognition, which analyzes multimedia content, including video (e.g., .mp4), audio (e.g., .mp3), and text (e.g., .txt) files, to identify and categorize emotions such as happiness, anger, sadness, etc.

This platform is intended for a wide range of applications, from assessing the psychological-emotional state of employees to customer service and feedback analysis (in the future), providing insights into emotional states across different contexts. It will have a user-friendly web interface for easy interaction, including features such as file uploading, emotion analysis, and detailed emotion reports with the ability to tag and categorize files for better organization and analysis.

Technical Requirements

Backend: Development using Node.js and Express for RESTful API services. Database: PostgreSQL for user data storage, file metadata, analysis results, and user tags or categories.

Frontend: A simple yet intuitive web interface for interacting with the platform. HTML/CSS + [Small frontend libraries].

Emotion analysis mechanism: Integration of machine learning models capable of analyzing textual, audio, and video content for emotion recognition. Human + OpenAI + Whisper.

Security: Implementation of standard security practices to protect data and authenticate users. ISO 27001.

Cloud Infrastructure: Deployment on Azure, utilizing services for computation, storage, and potentially machine learning capabilities (Azure Cognitive Services for pre-trained emotion recognition models).

Testing: Using Jest or Mocha for backend testing, and Selenium or Cypress for end-to-end web interface testing.

Project Objectives

With the help of this software, our client wanted to collect and process information about the internal state of employees, their levels of happiness, satisfaction, dissatisfaction, concerns, and other emotions related to work and life.

Working with this information could increase employee efficiency and happiness levels at work and in life.

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