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https://github.com/mohini1403/deep-facial-recognisation-system
This project implements a Siamese Neural Network for efficient image triplet analysis, seamlessly integrating OpenCV for image collection. Three categories of images - positive, negative, and anchor - are collected using OpenCV and preprocessed to standardize dimensions to 100x100 pixels in JPEG format.
https://github.com/mohini1403/deep-facial-recognisation-system
open-cv python siamese-neural-network tensforflow
Last synced: 7 days ago
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This project implements a Siamese Neural Network for efficient image triplet analysis, seamlessly integrating OpenCV for image collection. Three categories of images - positive, negative, and anchor - are collected using OpenCV and preprocessed to standardize dimensions to 100x100 pixels in JPEG format.
- Host: GitHub
- URL: https://github.com/mohini1403/deep-facial-recognisation-system
- Owner: MOHINI1403
- Created: 2024-05-14T10:51:22.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-05-14T12:32:37.000Z (6 months ago)
- Last Synced: 2024-05-15T16:05:48.269Z (6 months ago)
- Topics: open-cv, python, siamese-neural-network, tensforflow
- Language: Jupyter Notebook
- Homepage:
- Size: 8.35 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Facial Recognition System
   
## Description
This project implements a Siamese Neural Network for efficient image triplet analysis, seamlessly integrating OpenCV for image collection. Three categories of images - positive, negative, and anchor - are collected using OpenCV and preprocessed to standardize dimensions to 100x100 pixels in JPEG format.
### Research Paper
[Link to Research Paper](https://github.com/MOHINI1403/Deep-Facial-Recognisation-System/blob/main/Siamese_Neural_Network.pdf)
## Workflow Overview
1. **Data Collection:** Utilizing OpenCV, 300 images are collected from each category: positive, negative, and anchor.
2. **Preprocessing:** Images are resized to 100x100 pixels and converted to JPEG format.
3. **Dataset Creation:** Positive and negative image pairs are combined with anchor images, generating a labeled dataset. Positive pairs are labeled as 1, and negative pairs as 0.
4. **Training Data Preparation:** The dataset is split into training and testing sets in a 70-30 ratio and batched with a size of 8 for efficient processing.
5. **Siamese Model Architecture:** A Siamese neural network is constructed with an embedding layer for feature extraction and a custom distance layer for similarity computation.
6. **Model Training:** The Siamese model is trained on the prepared dataset, optimizing recall and precision metrics for performance evaluation.
7. **Real-Time Verification:** OpenCV is employed for real-time image verification, showcasing the practical application of the trained model.
### Embedding Layer
### Siamese Model
## Tech Stack
### Python
### Tensorflow
### Open-CV
## Usage
1. **Clone the Repository:**
```bash
git clone https://github.com/MOHINI1403/Deep-Facial-Recognisation-System.git
```
2. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
3. **Folder Structure:**
- **Data Folder:**
- Contains three sections: positive, negative, and anchor images.
- Use OpenCV to collect images for the positive and negative sections.
- Negative images can be downloaded [here](insert_link_to_download_negative_images).
- **Application Images Folder:**
- Contains two folders: input_image and verification_images.
- verification_images folder should contain around 50 images randomly selected from positive and anchor sets.
4. **Virtual Environment:**
- Create a virtual environment named faceid:
```bash
python -m venv faceid
```
- Activate the virtual environment:
- On Windows:
```bash
faceid\Scripts\activate
```
- On macOS and Linux:
```bash
source faceid/bin/activate
```
## Contribution
Contributions are welcome! Fork the repository, make your changes, and submit a pull request.
## Key Components
- **OpenCV Integration:** Efficient image collection and real-time verification.
- **Siamese Neural Network:** Architecture for learning image similarity through feature embeddings.
- **Custom Distance Layer:** Calculates the L1 distance between embeddings for similarity assessment.
- **Training Evaluation:** Metrics such as recall and precision used to assess model performance.
### Further Assitance :
Try Reading this Documentation :
