How to Train Your Own FaceID ConvNet Using TensorFlow Eager Execution

Facial recognition technology has rapidly advanced in recent years and is now deployed in a wide range of applications, from unlocking smartphones to identifying criminals. At the core of modern facial recognition systems are deep learning algorithms known as convolutional neural networks (CNNs).

In this post, I‘ll walk you through how to train your own CNN for facial recognition using TensorFlow‘s eager execution capabilities. By the end, you‘ll have a working facial recognition model that you can use for your own projects. Let‘s get started!

Overview of Facial Recognition with CNNs

Facial recognition is the task of identifying or verifying a person from a digital image or video frame. It‘s a challenging problem due to the wide variability in facial appearance, lighting conditions, poses, occlusions, and more.

Traditional facial recognition algorithms relied on hand-crafted feature extraction techniques. However, deep learning with CNNs has achieved state-of-the-art results by automatically learning hierarchical feature representations directly from data.

A typical deep learning approach to facial recognition consists of the following steps:

1. Obtain a large dataset of labeled facial images
2. Preprocess the images (alignment, normalization, etc.)
3. Train a CNN to map face images to compact feature embeddings
4. Compare feature embeddings to recognize identities

The key component is the CNN, which learns to extract discriminative facial features that can be used to distinguish between different individuals. Popular CNN architectures for facial recognition include FaceNet, DeepFace, and DeepID.

TensorFlow and Eager Execution

TensorFlow is an open-source library developed by Google for numerical computation and large-scale machine learning. It allows you to define computational graphs and automatically compute gradients for training.

Eager execution is a imperative programming environment that evaluates operations immediately, without building graphs. This makes it easy to get started with TensorFlow and debug models.

With eager execution, there are no sessions or placeholders. Instead, you can simply define and run computations using familiar Python control flow and NumPy-like operations. Eager execution is especially useful for research and experimentation.

Here‘s a simple example of defining and training a model with eager execution:

import tensorflow as tf

tf.enable_eager_execution()

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, input_shape=(8,), activation=‘relu‘),
    tf.keras.layers.Dense(1, activation=‘sigmoid‘)
])

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

def loss(model, x, y):
    y_ = model(x)
    return tf.losses.binary_crossentropy(y, y_)

def grad(model, x, y):
    with tf.GradientTape() as tape:
        loss_value = loss(model, x, y)
    return loss_value, tape.gradient(loss_value, model.trainable_variables)

for epoch in range(NUM_EPOCHS):
    for x, y in dataset:
        loss_value, grads = grad(model, x, y)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))

This code defines a simple sequential model, loss function, and gradient function. It then trains the model for some number of epochs using an optimizer. The tf.GradientTape() context records operations for automatic differentiation.

Now that you have a basic understanding of CNNs, facial recognition, and TensorFlow eager execution, let‘s see how to put it all together to build a facial recognition model.

Step 1: Obtain and Preprocess a Facial Image Dataset

The first step is to obtain a suitable dataset for training. For facial recognition, you‘ll need a large number of labeled images of faces. Some popular public datasets include:

• Labeled Faces in the Wild (LFW): 13,000+ images of 1,680 people
• VGGFace2: 3.31 million images of 9,131 subjects
• MS-Celeb-1M: 10 million images of 100,000 celebrities
• CASIA-WebFace: 494,414 images of 10,575 subjects

For this example, we‘ll use the VGGFace2 dataset. You can download it from the official website: http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/

Once you have the dataset, you‘ll need to preprocess it before training. This typically involves:

1. Detecting and aligning faces using a pretrained face detector (e.g. MTCNN, Haar cascades)
2. Resizing images to a consistent size (e.g. 224×224)
3. Normalizing pixel values to [0, 1]
4. Splitting the dataset into train/validation/test sets

Here‘s some example code for preprocessing with TensorFlow:

import tensorflow as tf

def load_image(image_path, image_size):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3) 
    image = tf.image.resize_image_with_crop_or_pad(image, image_size, image_size)
    image /= 255.0
    return image

def preprocess_dataset(image_paths, image_size, batch_size):
    path_ds = tf.data.Dataset.from_tensor_slices(image_paths)
    image_ds = path_ds.map(lambda x: load_image(x, image_size))
    return image_ds.batch(batch_size).prefetch(1)

This code defines functions to read an image from disk, resize it, normalize the pixel values, and create a batched dataset for training. You can apply a similar procedure to your own dataset.

Step 2: Define the CNN Model Architecture

Next, we need to define the architecture of our CNN model. For this example, we‘ll use a variant of the FaceNet model, which learns a mapping from face images to 128-dimensional embeddings.

Here‘s the model definition in TensorFlow eager execution:

import tensorflow as tf

def face_model(image_size):
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(64, (7,7), padding=‘same‘, activation=‘relu‘, input_shape=(image_size, image_size, 3)),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding=‘same‘),
        tf.keras.layers.Conv2D(64, (1,1), activation=‘relu‘),
        tf.keras.layers.Conv2D(192, (3,3), padding=‘same‘, activation=‘relu‘),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding=‘same‘),
        tf.keras.layers.Inception((64,), (96,128), (16,32), (32,)),
        tf.keras.layers.Inception((128,), (128,192), (32,96), (64,)),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding=‘same‘),
        tf.keras.layers.Inception((192,), (96,208), (16,48), (64,)),
        tf.keras.layers.Inception((160,), (112,224), (24,64), (64,)),
        tf.keras.layers.Inception((128,), (128,256), (24,64), (64,)),
        tf.keras.layers.Inception((112,), (144,288), (32,64), (64,)),
        tf.keras.layers.Inception((256,), (160,320), (32,128), (128,)),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2), padding=‘same‘),
        tf.keras.layers.Inception((256,), (160,320), (32,128), (128,)),
        tf.keras.layers.Inception((384,), (192,384), (48,128), (128,)),
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(128),
    ])
    return model

This model architecture consists of a series of convolutional, pooling, and inception layers followed by a global average pooling layer and a final dense layer to output 128-dimensional embeddings. The inception layers allow the network to learn multi-scale features.

You can modify this architecture or experiment with other ones like ResNet or MobileNet to see what works best for your application.

Step 3: Train the Model

With our model defined, the next step is to train it on our dataset. We‘ll use a triplet loss function, which encourages the model to learn an embedding space where faces of the same person are close together and faces of different people are far apart.

Here‘s the code to train the model:

import tensorflow as tf

ALPHA = 0.2
LEARNING_RATE = 0.1
NUM_EPOCHS = 50

def triplet_loss(anchor, positive, negative):
    pos_dist = tf.reduce_sum(tf.square(anchor - positive), axis=-1)
    neg_dist = tf.reduce_sum(tf.square(anchor - negative), axis=-1)
    basic_loss = pos_dist - neg_dist + ALPHA
    loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
    return loss

def train_step(model, data):
    anchor, positive, negative = data    

    with tf.GradientTape() as tape:
        loss = triplet_loss(model(anchor), model(positive), model(negative))
        grads = tape.gradient(loss, model.trainable_variables)

    optimizer.apply_gradients(zip(grads, model.trainable_variables))

    return loss

model = face_model(IMAGE_SIZE)
optimizer = tf.train.AdamOptimizer(LEARNING_RATE)

for epoch in range(NUM_EPOCHS):
    print("Epoch: {}".format(epoch))

    for batch in dataset:
        loss = train_step(model, batch)
        print("Triplet loss: {:.3f}".format(loss))

    print("\n")

This training loop iterates over the dataset for a number of epochs. For each batch, it computes the triplet loss and gradients, and updates the model parameters using the Adam optimizer. The triplet loss function encourages the anchor and positive embeddings to be close together while the anchor and negative embeddings are far apart.

You‘ll need to generate triplets of anchor, positive, and negative examples from your dataset. One way to do this is to randomly select two images of the same person for the anchor and positive, and an image of a different person for the negative.

After training, you can evaluate your model on a held-out test set and visualize the learned embedding space using techniques like t-SNE to verify that it clusters similar faces together.

Step 4: Use the Trained Model for Facial Recognition

Finally, with a trained model, you can deploy it for facial recognition. The basic process is:

1. Pass a query face image through the model to get its embedding
2. Compare the query embedding to a database of known embeddings
3. Find the closest match(es) based on Euclidean distance

Here‘s a simple example of using a trained model for facial recognition:

import tensorflow as tf
import numpy as np

def recognize_face(model, face_image, known_embeddings, known_labels, threshold=0.6):
    embedding = model(face_image)
    distances = tf.reduce_sum(tf.square(embedding - known_embeddings), axis=1)
    index = tf.argmin(distances).numpy() 

    if distances[index] < threshold:
        return known_labels[index]
    else:
        return "Unknown"

query_image = load_image("path/to/query/image.jpg", IMAGE_SIZE)
label = recognize_face(model, query_image, known_embeddings, known_labels) 
print("Predicted identity: {}".format(label))

This code takes a query face image, passes it through the model to get its embedding vector, compares it to a set of known embeddings, and returns the identity label of the closest match if it‘s below a distance threshold. The known embeddings would come from passing a set of reference images through the model.

You can further improve the recognition accuracy by using multiple reference images per person, handling unknown identities, and updating the database over time.

Challenges and Considerations

While facial recognition with deep learning can be highly accurate, there are several challenges and considerations to keep in mind when developing these systems:

• Fairness and bias: Models can exhibit biases based on race, gender, age, etc. It‘s important to use diverse datasets and audit for fairness.

• Privacy and security: Facial recognition raises privacy concerns around consent, data collection, and misuse. Proper safeguards and policies are needed.

• Robustness: Models can be fooled by adversarial examples, occlusions, and other factors. Techniques like anomaly detection can help.

• Computational efficiency: Training and inference can be computationally expensive, especially with large datasets and complex models. Quantization and compression can help reduce costs.

It‘s important for practitioners to be aware of these issues and to develop facial recognition systems responsibly and ethically.

Future Developments

Facial recognition technology continues to advance rapidly. Some exciting future developments include:

• 3D face modeling for improved accuracy and robustness to pose
• Federated learning to train models on decentralized data
• Unsupervised and self-supervised learning to reduce annotation costs
• Explainable and interpretable models for transparency and accountability
• Efficient models for deployment on edge devices and mobile phones

As a developer in this space, it‘s important to stay up-to-date with the latest research and best practices. With proper care and innovation, facial recognition has the potential to enable many beneficial applications in society.

Conclusion

In this post, you learned how to train your own facial recognition model using deep learning with TensorFlow eager execution. Specifically, you saw how to:

1. Obtain and preprocess a facial image dataset
2. Define a CNN architecture for learning face embeddings
3. Train a model using triplet loss
4. Deploy the trained model for facial recognition

You also learned about some of the key challenges and considerations in developing facial recognition systems, as well as future research directions.

Facial recognition is a powerful technology that requires responsible development and deployment. By starting with the techniques covered here and staying attuned to the evolving technical and social landscape, you can build accurate, reliable, and ethical facial recognition systems. Good luck!