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M.E. Project Report
M.E. Skin Tone Based Clothing Recommendation System Final Year Project Report
Get structured M.E. Skin Tone Based Clothing Recommendation System Final Year Project Report Final Year Project Report and Documentation with project objectives, methodology, system design, diagrams, implementation details, testing and complete project explanations. Suitable as a learning, documentation and project-presentation resource for students working on related final year projects.
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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—
Abstract
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—
Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
Skin Tone Based Clothing Recommendation System is a complete MERN Stack fashion e-commerce and personalized clothing recommendation application that recommends clothing colours according to a customer’s skin tone category and undertone.
The recommendation engine uses transparent rule-based colour matching stored in MongoDB. It does not depend on a third-party recommendation API, external machine-learning service, or cloud AI model. Customers can manually select their skin tone and undertone or optionally upload a photo for local RGB/HSV-based colour sampling using Sharp.
After a skin profile is created, the platform evaluates product colours and displays recommendation levels such as:
-
Best Match
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Good Match
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Regular Match
The system also includes outfit pairing suggestions based on administrator-defined upper- and lower-clothing colour rules. Alongside recommendations, SmartWear operates as a complete fashion e-commerce system with brands, categories, products, size and colour variants, inventory, wishlist, shopping bag, delivery addresses, COD checkout, order tracking, product reviews, homepage content, reporting, and a dedicated Admin Console.
This project is suitable for B.Tech, M.Tech, BCA, MCA, BE, ME, BSc, MSc, Computer Science, IT, Web Development, E-Commerce, and MERN Stack students searching for a final year project, major project, minor project, source code, and project report based on personalized fashion recommendations, skin-tone analysis, clothing colour matching, fashion e-commerce, and online shopping.
Login Credentials
Administrator
| Field | Value |
|---|---|
[email protected] |
|
| Password | Admin@123 |
Customer
| Field | Value |
|---|---|
[email protected] |
|
| Password | User@123 |
Related & Popular Reports
View all
Admin Dashboard
Admin Dashboard includes:
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User statistics
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Product statistics
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Order statistics
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Sales aggregates
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Low-stock products
-
Recent orders
Brand Management
Admin can:
-
Create brand
-
View brands
-
Edit brand
-
Delete brand
-
Upload logo
-
Activate/deactivate brand
-
Export brand list to CSV
Category Management
Admin can:
-
Create category
-
Edit category
-
Delete category
-
Upload image
-
Activate/deactivate category
-
Export list to CSV
Product Management
Admin can:
-
Create product
-
View products
-
Edit product
-
Delete product
-
Select brand
-
Select category
-
Add price
-
Add multiple images
-
Add sizes
-
Add colours
-
Configure stock for colour/size combinations
-
Activate/deactivate product
-
Manage product information
The product editor uses structured variant controls rather than requiring Admin to manually enter raw JSON.
Inventory Management
Admin can:
-
View product inventory
-
Filter low-stock products
-
View colour variants
-
View size variants
-
Adjust stock per colour
-
Adjust stock per size
-
Monitor stock availability
Skin Tone Category Management
Admin can perform full CRUD for skin-tone categories.
These categories are used by the recommendation engine.
Undertone Management
Admin can:
-
Create undertone
-
View undertones
-
Edit undertone
-
Delete undertone
Undertones combine with skin-tone categories to determine recommendation rules.
Skin Tone Colour Rule Management
Admin can create transparent recommendation mappings between:
-
Skin tone category
-
Undertone
-
Recommended colours
-
Match levels
This allows recommendation behaviour to be adjusted without changing source code.
Outfit Matching Rule Management
Admin can configure compatibility between:
-
Upper clothing category/colour
-
Lower clothing category/colour
These rules drive outfit pairing suggestions.
Admin Order Management
Admin can:
-
View orders
-
Filter by order status
-
Search by order ID
-
Search visible order data in UI
-
Open order details
-
View line items
-
View customer address
-
Update order status
Stock adjustments are tied to Confirm and Cancel operations where applicable.
Customer Management
Admin can:
-
View customers
-
Search by name
-
Search by email
-
View profile summary
-
View customer orders
-
Activate customer
-
Deactivate customer
Inactive customers cannot log in.
Review Moderation
Admin can:
-
View reviews
-
Filter visible reviews
-
Filter hidden reviews
-
Hide review
-
Unhide review
-
Delete review
Homepage Management
Admin can manage homepage section types such as:
-
Banner
-
Promotional text
-
Featured products
Banner images can also be uploaded.
Site Settings
Admin can manage:
-
Website copy
-
Contact details
-
Policies
-
Logo
-
Favicon
-
Other site configuration
Admin Reports
The reporting module includes report types such as:
-
Orders
-
Sales
-
Products
-
Stock
-
Users
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Most purchased
-
Category products
-
Brand products
-
Skin-tone preferences
-
Recommended colours
Optional date-range filtering is supported where applicable.
Reports can be downloaded as CSV.
CSV Export
CSV export is implemented for:
-
Several Admin listing screens
-
Admin Reports module
Not implemented:
-
PDF reports
-
Excel
.xlsx -
Print-specific reports
Authentication & Security
The project includes:
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JWT authentication
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Separate customer/Admin token storage
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bcryptjs password hashing
-
Admin authorization middleware
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Active/inactive account validation
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Login/register rate limiting
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Helmet
-
CORS restrictions
-
API validation
-
Local upload validation
Login/register rate limiting is documented as:
50 requests per 15 minutes per IP.
Customer Registration
Customers can register using:
-
Name
-
Email
-
Mobile number
-
Gender
-
Password
The documented minimum password length is 6 characters.
Customer Login
-
Customer login
-
JWT authentication
-
Customer token stored separately
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Active/inactive account check
-
Logout
-
Protected customer routes
The browser uses:
token
for the customer JWT.
Skin Tone Profile
A major feature of SmartWear is the customer skin profile.
Users can configure:
-
Skin tone category
-
Undertone
The skin profile becomes the basis of the clothing-colour recommendation engine.
Manual Skin Tone Selection
Customers can manually choose their:
-
Skin tone category
-
Undertone
These selections are mapped to recommendation rules stored by the administrator.
Skin Tone Detection from Photo
The system also supports an optional photo-based flow.
Process:
Upload Skin Photo
↓
Local Image Processing
↓
Sharp RGB/HSV Sampling
↓
Suggested Skin Information
↓
User Skin Profile
↓
Recommendation Rules
Image processing is performed locally using Sharp rather than an external AI or image-analysis API.
Clothing Colour Recommendation Engine
The recommendation engine is based on:
-
Skin tone category
-
Undertone
-
Admin-defined colour rules
-
Product colour
-
RGB colour distance
The system loads the relevant SkinToneColorRule and evaluates clothing colours according to configured recommendation data.
Match Levels
Product colours can be displayed as:
-
Best Match
-
Good Match
-
Regular Match
The recommendation algorithm compares product colours with the configured recommended-colour rules.
Recommended Products
The customer recommendation page can display products ranked or labelled according to their compatibility with the customer's skin profile.
Features include:
-
Personalized product feed
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Colour-match badges
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Skin-tone based filtering
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Product compatibility display
-
Recommended colour palette
Recommended Colour Palette
The customer account includes a dedicated palette view where users can see:
-
Current skin tone
-
Undertone
-
Recommended colours
This helps customers understand which colours are most compatible with their configured profile.
Recommendation History
The system stores previous recommendation activity using:
RecommendationHistory
This provides a history of recommendation runs associated with the customer.
Outfit Recommendation System
SmartWear includes an Outfit Builder.
The outfit recommendation system uses:
OutfitMatchingRule
to match upper and lower clothing colours.
Example workflow:
Select Upper Clothing
↓
Read Upper Colour
↓
Apply Outfit Matching Rule
↓
Find Compatible Lower Colours
↓
Show Outfit Suggestions
This is also rule-based and configured by Admin.
Fashion Product Catalog
Products can contain:
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Product name
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Brand
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Category
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Gender
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Price
-
Images
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Multiple colours
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Multiple sizes
-
Stock by colour and size
-
Featured status
-
Active status
-
Customer reviews
Product Variants
The inventory model supports clothing variants according to:
-
Colour
-
Size
Each colour can contain stock values by size.
This makes inventory more accurate than a single stock quantity for the entire product.
Wishlist
Customers can:
-
Add product to wishlist
-
View wishlist
-
Remove product
-
Move product to shopping bag
Shopping Bag / Cart
Customers can:
-
Add product
-
Select colour
-
Select size
-
Select quantity
-
Update quantity
-
Remove product
-
Clear shopping bag
-
View cart count
The storefront header synchronizes cart and wishlist counts.
Delivery Address Management
Customers can:
-
Create address
-
View addresses
-
Edit address
-
Delete address
-
Set default address
-
Select address during checkout
Checkout
The checkout workflow includes:
Recommended / Catalog Product
↓
Select Colour & Size
↓
Add to Bag
↓
Review Shopping Bag
↓
Select Delivery Address
↓
Cash on Delivery
↓
Place Order
The documented payment method is Cash on Delivery only.
Cash on Delivery
SmartWear does not use an online payment gateway.
Implemented:
-
Cash on Delivery
Not implemented:
-
Razorpay
-
Stripe
-
PayPal
-
Card payment
-
UPI gateway
Order Workflow
A typical order workflow is:
Order Placed
↓
Pending
↓
Confirmed
↓
Further Fulfilment Statuses
↓
Shipped
↓
Delivered
Stock is decremented when Admin moves an order to Confirmed.
If an applicable confirmed order is cancelled, previously adjusted stock is restored.
Order Management for Customers
Customers can:
-
View order list
-
Open order detail
-
Track status
-
View purchased products
-
View delivery information
-
Cancel eligible order
By default, cancellation is permitted before the configured status:
Shipped
The threshold can be controlled using ORDER_CANCEL_BEFORE_STATUS.
Product Reviews
Customers can:
-
Create product review
-
View own reviews
-
Update own review
-
Delete own review
Review eligibility requires:
-
Order status = Delivered
-
Purchased order contains the product
Customer Dashboard
The customer dashboard includes information such as:
-
Orders
-
Shopping bag count
-
Wishlist count
-
Quick account navigation
Customer Profile
Customers can:
-
Update profile
-
Change password
-
Verify current password
-
Upload avatar
Technology Used
| Layer | Technology |
|---|---|
| Frontend | React 19 |
| Build Tool | Vite 8 |
| Routing | React Router 7 |
| HTTP Client | Axios |
| Styling | Tailwind CSS 4 |
| UI | Headless UI |
| Icons | Heroicons |
| Forms | React Hook Form |
| Validation | Zod / express-validator |
| Backend | Node.js |
| Framework | Express 5 |
| Database | MongoDB |
| ODM | Mongoose 9 |
| Authentication | JWT |
| Password Security | bcryptjs |
| Image Upload | Multer |
| Image Processing | Sharp |
| Security | Helmet, CORS, express-rate-limit |
| Architecture | MERN Stack |
Step 1 — Open Project
cd "SmartWear– Skin Tone Based Clothing Recommendation System"
Step 2 — Install Dependencies
npm run install:all
This installs root, frontend, and backend dependencies.
Step 3 — Configure Environment
Windows:
copy server\.env.example server\.env
Example:
PORT=5000
MONGO_URI=mongodb://127.0.0.1:27017/smartwear
JWT_SECRET=change-this-to-a-long-random-secret
CLIENT_URL=http://localhost:5173
LOW_STOCK_THRESHOLD=5
ORDER_CANCEL_BEFORE_STATUS=Shipped
Step 4 — Start MongoDB
Ensure MongoDB is running at the configured MONGO_URI.
Step 5 — Seed Development Data
npm run seed
The seed loads Indian demonstration data including:
-
Users
-
Products
-
Brands
-
Categories
-
Orders
-
Carts
-
Wishlists
-
Reviews
-
Skin-tone rules
-
Outfit rules
-
CMS data
The seed wipes application collections before rebuilding demo data, and remote database resets require an explicit safety flag.
Step 6 — Start Application
npm run dev
This starts:
Backend API: http://localhost:5000
Frontend: http://localhost:5173
README Included
Setup Support Available
Administrator
| Field | Value |
|---|---|
[email protected] |
|
| Password | Admin@123 |
Customer
| Field | Value |
|---|---|
[email protected] |
|
| Password | User@123 |