Unsupervised Face Clustering Pipeline in OpenCV

Clustering faces essentially means grouping images that contain the same person. This can be an important step for large-scale image organization, people counting, and other applications. In OpenCV, you can perform face clustering using deep learning models for feature extraction and then apply clustering algorithms like KMeans.

Here's a step-by-step tutorial to build an unsupervised face clustering pipeline using OpenCV:

Prerequisites:

Install necessary libraries:

pip install opencv-python opencv-python-headless scikit-learn

Step-by-Step Tutorial:

Import necessary libraries:

import cv2
import numpy as np
from sklearn.cluster import KMeans

Load Pre-trained Model for Feature Extraction:

OpenCV provides a pre-trained model called "ResNet-34" that can be used for extracting features from faces.

model = cv2.dnn.readNetFromTorch("path_to_resnet_model")

Replace path_to_resnet_model with the actual path of the model. You can usually get this model from OpenCV's GitHub or other model repositories.

Extract Features from Faces:

Define a function to preprocess the image and extract features.

def get_features(image_path, model):
    img = cv2.imread(image_path)
    blob = cv2.dnn.blobFromImage(img, 1.0, (224, 224), (104, 117, 123))
    model.setInput(blob)
    return model.forward().flatten()

Process Images and Extract Features:

You can loop through your dataset to extract features and save them in a list.

image_paths = ["path1", "path2", ...]  # Replace with your image paths
features = []

for path in image_paths:
    features.append(get_features(path, model))

Cluster Faces Using KMeans:

Use the extracted features for clustering:

kmeans = KMeans(n_clusters=num_clusters)
labels = kmeans.fit_predict(features)

Here, num_clusters is the number of persons (clusters) you think might be there in your dataset. You can also use algorithms like the Elbow Method to determine the optimal number of clusters.

Group Images by Label:

Group the images based on the labels obtained from clustering.

clusters = {}
for img_path, label in zip(image_paths, labels):
    if label in clusters:
        clusters[label].append(img_path)
    else:
        clusters[label] = [img_path]

You can then display or process these clustered faces as needed.

Conclusion:

This pipeline provides a basic approach to face clustering. For more accurate results, consider:

Using more sophisticated deep learning models for feature extraction.
Trying hierarchical clustering for better cluster structure in the presence of varying numbers of images per individual.
Incorporating face detection in the pipeline to ensure you're extracting features only from the facial region and not the entire image.

Remember, clustering accuracy might vary based on image quality, variations in poses, and other factors. Fine-tuning and experimenting with different methods/models is the key to achieving better accuracy.

Face clustering with k-means in OpenCV:

Description: Applying k-means clustering to group faces based on their features extracted from images.

Code:

import cv2
import numpy as np
from sklearn.cluster import KMeans

# Load face images (use face detection to extract faces)
faces = ...

# Extract facial features (e.g., using face embeddings)
features = ...

# Apply k-means clustering
k = 3  # Number of clusters
kmeans = KMeans(n_clusters=k)
labels = kmeans.fit_predict(features)

# Visualize the clusters
for i in range(k):
    cluster_faces = faces[labels == i]
    for face in cluster_faces:
        cv2.imshow(f'Cluster {i}', face)
        cv2.waitKey(0)

OpenCV facial recognition clustering:

Description: Implementing facial recognition followed by clustering to group identified faces.

Code:

import cv2
import face_recognition

# Load images and encode facial features
known_faces = ...
unknown_faces = ...

# Recognize faces using face_recognition library
face_encodings = [face_recognition.face_encodings(face)[0] for face in unknown_faces]

# Apply k-means clustering
k = 3  # Number of clusters
kmeans = KMeans(n_clusters=k)
labels = kmeans.fit_predict(face_encodings)

# Visualize the clusters
for i in range(k):
    cluster_faces = unknown_faces[labels == i]
    for face in cluster_faces:
        cv2.imshow(f'Cluster {i}', face)
        cv2.waitKey(0)

OpenCV face clustering with DBSCAN:

Description: Using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for face clustering in OpenCV.

Code:

import cv2
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

# Load face images and extract features
faces = ...
features = ...

# Scale features
features = StandardScaler().fit_transform(features)

# Apply DBSCAN clustering
dbscan = DBSCAN(eps=0.5, min_samples=5)
labels = dbscan.fit_predict(features)

# Visualize the clusters
for i in range(len(np.unique(labels))):
    cluster_faces = faces[labels == i]
    for face in cluster_faces:
        cv2.imshow(f'Cluster {i}', face)
        cv2.waitKey(0)

OpenCV face clustering GitHub repository: