Design and Implementation of Intelligent Dressing Mirror Based on Raspberry Pi 4B

Project Code :TEMBMA3832

Objective

The objective of this project is to develop an intelligent dressing mirror using Raspberry Pi 4B that enhances personalized smart-home experiences through cloud connectivity and real-time sensing. The system integrates Alibaba Cloud’s MQTT service for managing clothing data and uses onboard sensors and cameras to collect environmental and user information. By analyzing these inputs, the mirror provides context-aware outfit recommendations along with essential updates such as weather, date, and schedules, offering a low-power, convenient, and personalized home-automation solution.

Abstract

The Design and Implementation of an Intelligent Dressing Mirror Based on Raspberry Pi 4B is an interactive smart system that enhances user experience using computer vision and emotion recognition. The system uses a Raspberry Pi 4B as the main controller along with a USB camera to capture the user’s facial expressions in real time. Based on detected emotions such as happy, sad, or neutral, the system controls RGB LEDs to display different colours and a speaker to provide voice feedback or motivational messages. The mirror acts as a smart assistant that responds to the user’s mood, making daily dressing and grooming more engaging and personalized. Python-based image processing and machine learning techniques are used

for facial expression detection, enabling an intelligent and responsive smart mirror system.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Hardware components:

Raspberry Pi 4B
USB Camera
LED Lights
Speaker
Mirror Glass Setup
Power Supply
12V Adapter
Connectors – 30

Software components:

Raspbian OS
Python

Learning Outcomes

Understand Raspberry Pi architecture and GPIO configuration
Learn how to install and configure Raspbian OS and required Python libraries
Interface analog sensors with Raspberry Pi using MCP3008 ADC
Implement image classification using Artificial Neural Networks
Develop real-time skin analysis using USB camera input
Build automated health screening systems with display and alert features
Integrate temperature and heartbeat monitoring in diagnostic systems
Analyze and interpret classification output for healthcare applications
About Project Development Life Cycle:
Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
Schematic preparation
Code development and debugging
Hardware development and debugging
Development of the Project and Output testing
Practical exposure to:
Hardware and software tools.
Solution providing for real time problems.
Working with team/ individual.
Work on Creative ideas.
Project development Skills:
Problem analyzing skills
Problem solving skills
Creativity and imaginary skills
Programming skills
Deployment
Testing skills
Debugging skills
Project presentation skillsUnderstand Raspberry Pi architecture and GPIO configuration
Learn how to install and configure Raspbian OS and required Python libraries
Interface analog sensors with Raspberry Pi using MCP3008 ADC
Implement image classification using Artificial Neural Networks
Develop real-time skin analysis using USB camera input
Build automated health screening systems with display and alert features
Integrate temperature and heartbeat monitoring in diagnostic systems
Analyze and interpret classification output for healthcare applications
About Project Development Life Cycle:

Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
Schematic preparation
Code development and debugging
Hardware development and debugging
Development of the Project and Output testing