Computer & IT · Machine learning and AI · Final year project
Facial Emotion Detection using Convolutional Neural Network
A deep learning model that reads a person's emotion from their face. A convolutional neural network trained on labelled face images classifies expressions, which can drive mood-based music, customer feedback or well-being alerts.
What is the Facial Emotion Detection using Convolutional Neural Network project?
A deep learning model that reads a person's emotion from their face. A convolutional neural network trained on labelled face images classifies expressions, which can drive mood-based music, customer feedback or well-being alerts.
Key facts
- Type
- Final year project · Advanced
- Suitable for
- Diploma capstone, BCA, B.Sc, B.E / B.Tech final year and MCA
- Build time
- 6 to 8 weeks
- Tech stack
- Deep learning (CNN) and Network simulation
- Guidance from
- ₹2,499 (AI / ML Mini Project)
How the Facial Emotion Detection using Convolutional Neural Network works
- Images are collected and labelled into classes, then resized and augmented.
- A convolutional neural network (often a fine-tuned MobileNet or ResNet) is trained on the training split.
- Accuracy, precision, recall and the confusion matrix are measured on unseen test images.
- The trained model is loaded in a Flask or Streamlit app where the user uploads an image or uses the webcam and sees the prediction.
Block diagram
User
Student / customerAdminDeep learning (CNN)
PythonTensorFlow / Keras or PyTorchOpenCVData
Trained modelTech stack and tools
| Layer | Tools |
|---|---|
| Deep learning (CNN) | Python, TensorFlow / Keras or PyTorch, OpenCV, Google Colab GPU |
| Network simulation | NS2 / NS3, TCL or C++ scripts, NAM / NetAnim, AWK and Xgraph for results |
Any of these can be swapped for the stack your college prescribes. Requirements: a laptop with 8 GB RAM, a free Google Colab GPU for training, and free, open-source tools only.
How much does this project cost?
| Option | What you get | Cost |
|---|---|---|
| AI / ML Mini Project | Python / Jupyter notebook, Cleaned dataset and trained model, Simple web demo (Streamlit) | ₹2,499 |
| Advanced Plan | 100% working source code, Black book / project report (100–150 pages) PDF, Step-by-step code explanation | ₹9,999 |
| Premium Plan | 100% working source code, GitHub + zip delivery, Black book (100–200 pages) | ₹11,999 |
No GST and no hidden charges. Use the cost calculator for a quote with your deadline and add-ons, or see all plans.
Download the project brief (PDF)
Summary, working, tech stack and viva questions on one branded PDF to show your guide or team. To get it developed, contact us on WhatsApp.
What you submit with this project
- Source code with setup guide and a GitHub repository
- Project report or black book in your university's chapter order (DFD, ER, UML diagrams, test cases)
- Presentation for the review and final viva
- Synopsis for topic approval, if your college asks for one
Computer & IT viva questions on this project
What is the aim of the Facial Emotion Detection using Convolutional Neural Network project?
A deep learning model that reads a person's emotion from their face. A convolutional neural network trained on labelled face images classifies expressions, which can drive mood-based music, customer feedback or well-being alerts.
Explain the working of your project in short.
Images are collected and labelled into classes, then resized and augmented. A convolutional neural network (often a fine-tuned MobileNet or ResNet) is trained on the training split. Accuracy, precision, recall and the confusion matrix are measured on unseen test images. The trained model is loaded in a Flask or Streamlit app where the user uploads an image or uses the webcam and sees the prediction.
How does a convolutional neural network work?
Convolution layers slide small filters over the image to detect edges and textures, pooling layers reduce the size, and fully connected layers combine the features to classify the image. Training adjusts the filter weights with backpropagation.
What is overfitting and how did you prevent it?
Overfitting is when the model memorises training data and fails on new data. We used data augmentation, dropout, early stopping and a separate validation set to control it.
What parameters did you measure in the simulation?
Packet delivery ratio, end-to-end delay, throughput, packet loss and energy consumption, compared between the existing protocol and the proposed one.
What is the difference between proactive and reactive routing?
Proactive protocols (DSDV, OLSR) keep routes to all nodes updated at all times. Reactive protocols (AODV, DSR) find a route only when data needs to be sent, which saves overhead in mobile networks.
Which SDLC model did you follow?
We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.
How did you test the project?
Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.
What are the limitations and future scope?
The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.
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