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https://github.com/ravindramohith/find_me_out
Face recognition system using a Convolutional Neural Network (CNN) with TensorFlow and OpenCV
https://github.com/ravindramohith/find_me_out
convolutional-neural-networks deep-learning face-recognition keras machine-learning opencv tensorflow
Last synced: 5 days ago
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Face recognition system using a Convolutional Neural Network (CNN) with TensorFlow and OpenCV
- Host: GitHub
- URL: https://github.com/ravindramohith/find_me_out
- Owner: ravindramohith
- Created: 2024-08-12T20:02:49.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-12T20:45:20.000Z (3 months ago)
- Last Synced: 2024-10-11T08:23:00.450Z (28 days ago)
- Topics: convolutional-neural-networks, deep-learning, face-recognition, keras, machine-learning, opencv, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 83 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Find Me Out
## Project Description
"Find Me Out" is a face recognition system that uses a novel feature extractor and classifier to identify celebrities from a dataset of 400 images. This system achieves a high accuracy of 93%, making it robust and reliable for face recognition tasks.
## Key Features
- **High Accuracy**: Achieved 93% accuracy in recognizing faces using a Convolutional Neural Network (CNN).
- **Enhanced Preprocessing**: Utilized OpenCV for data augmentation, grayscale conversion, and resizing to ensure consistent input quality.
- **Advanced Training**: Leveraged TensorFlow with the Adam optimizer and Sparse Categorical Cross-Entropy loss function for effective multi-class face recognition.
## Installation
To run this project, you'll need to set up a Python environment with the required dependencies. Here's how you can do it:
1. Clone the repository:
```bash
git clone https://github.com/yourusername/find_me_out.git
cd find_me_out
```
## Usage
1. **Mount Google Drive**: Ensure your dataset is stored in Google Drive and mount it using the provided code snippet.
```python
from google.colab import drive
drive.mount('/content/drive')
```
2. **Data Loading and Preprocessing**: The system loads and preprocesses images for training.
```python
import os
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
directory = "/content/drive/MyDrive/Colab Notebooks/Data/"
celebrities = ["Scarlett_Johansson", "Elizabeth_Olsen", "Christian_Bale", "Chris_Evans"]
data = []
labels = []
for i, celebrity in enumerate(celebrities):
celebrity_dir = os.path.join(directory, celebrity)
for h in range(1, 101):
filename = f"IMG_{h}.jpg"
if filename.endswith(".jpg"):
file_path = os.path.join(celebrity_dir, filename)
print(file_path)
image = cv2.imread(file_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale
image = cv2.resize(image, (224, 224)) # Resize the image to a desired size
data.append(image)
labels.append(i)
data = np.array(data)
labels = np.array(labels)
x_train, x_test, y_train, y_test = train_test_split(
data, labels, test_size=0.25
)
train_data = x_train.astype("float32") / 255.0
test_data = x_test.astype("float32") / 255.0
train_data = np.expand_dims(train_data, -1)
test_data = np.expand_dims(test_data, -1)
```
3. **Model Training**: Train the CNN model using the provided training code.
```python
model = keras.Sequential(
[
keras.Input(shape=(224, 224, 1)),
layers.Conv2D(16, 3, activation="relu"),
layers.MaxPool2D(),
layers.Conv2D(24, 3, activation="relu"),
layers.Conv2D(32, 3, activation="relu"),
layers.Conv2D(32, 3, activation="relu"),
layers.MaxPool2D(),
layers.Conv2D(64, 3, activation="relu"),
layers.MaxPool2D(),
layers.Flatten(),
layers.Dense(4, activation="softmax"),
]
)
print(model.summary())
# Compile and train the model
model.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=["accuracy"],
optimizer=keras.optimizers.Adam(learning_rate=0.001),
)
model.fit(train_data, y_train, batch_size=300, epochs=200, verbose=2)
# Evaluate the model on the test set
model.evaluate(test_data, y_test, batch_size=300, verbose=2)
```
4. **Prediction**: Use the trained model to predict and identify the celebrity in a new image.
```python
from google.colab.patches import cv2_imshow
test_image_path = (
directory+"Chris_Evans/IMG_105.jpg" # Replace with the path to your test image
)
test_image = cv2.imread(test_image_path)
# cv2.imshow("Chris Evans",test_image)
test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) # Convert to grayscale
test_image = cv2.resize(
test_image, (224, 224)
) # Resize the image to match the model's input size
test_image = np.expand_dims(test_image, axis=0) # Add a batch dimension
test_image = test_image.astype("float32") / 255.0 # Normalize pixel values
# Make predictions on the test image
predictions = model.predict(test_image)
predicted_celebrity = celebrities[np.argmax(predictions[0])]
cv2_imshow(cv2.imread(test_image_path))
print("Predicted celebrity:", predicted_celebrity)
```