Face Aging Using Conditional GANs

Author(s): Manish Nayak

Originally published on Towards AI.

Age-cGANs Explained U+007C Towards AI

An Introduction to Age-cGANs

Introduction

Conditional GANs (CGANs) are extensions of the GANs model. You can read about Conditional GANs in my previous post here. In this post, I will try to explain how we can implement a CGANs to perform automatic face aging. Face Aging cGAN(Age-cGANs) introduced by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay, in their paper with titled Face Aging With Conditional Generative Adversarial Networks.

High-Level CGANs’s Architecture Diagram

Age-cGANs’s Architecture

The Face Aging-cGan has four networks.

An Encoder : It learns the inverse mapping of input face images and the age condition with the latent vector Z.

• Encoder network generates a latent vector of the input images. The Encoder network is a CNN which takes an image of a dimension of (64, 64, 3) and converts it into a 100-dimensional vector.
• There are four convolutional blocks and two dense layers.
• Each convolutional block has a convolutional layer, followed by a batch normalization layer, and an activation function except the first convolutional layer.

A FaceNet : It is a facial recognition network that learns the difference between an input image x and a reconstructed image x’.

• FaceNet recognizes a person’s identity in a given image.
• A pre-trained Inception, ResNet-50 or Inception-ResNet-2 model without fully connected layers can be used.
• The extracted embeddings for the real image and the reconstructed image can be calculated by calculating the Euclidean distance of the embeddings.

A Generator Network: It takes a hidden representation of a face image and a condition vector as input and generates an image.

• The Generator network is a CNN and it takes a 100-dimensional latent vector and a condition vector y, and tries to generate realistic images of a dimension of (64, 64, 3)
• The Generator network has dense, upsampling, and convolutional layers.
• It takes two input one is a noise vector and second is a condition vector.
• The condition vector is the additional information that is provided to the network. For the Age-cGAN, this will be the age.

A Discriminator Network: It tries to discriminate between the real images and the fake images.

• The Discriminator network is a CNN and it predicts the given image is real or fake.
• There are several convolutional blocks. Each convolutional block contains a convolutional layer followed by a batch normalization layer, and an activation function, except the first convolutional block, which doesn’t have the batch normalization layer.

Aging-cGANs’s training

Age-cGAN has four networks, which trained in three steps.

Conditional GAN training: Generator and Discriminator network training.

• cGAN training can be expressed as an optimization of the function v(θG, θD), where θG and θD are parameters of G and D, respectively.

Where

• log D(x,y) is the loss for the Discriminator model.
• log(1-D(G(x,y’),y’)) is the loss for the Generator model.
• P(data) is the distribution of all possible images.

Initial latent vector approximation: Encoder network training.

• Initial latent vector approximation method uses to approximate a latent vector to optimize the reconstruction of face images.
• The encoder is a neural network which approximates a latent vector.
• We train the encoder network on the generated images and real images.
• Once trained, the encoder network will start generating latent vectors from the learned distribution.
• The training objective function for training the encoder network is the Euclidean distance loss

Latent vector optimization: Optimization of both Encoder and Generator network together.

Where

• FR is the face recognition network to recognize a person’s identity in an input face image x
• Above equation is the Euclidean distance between the real image x and the reconstructed images x’ and it should be minimal.
• Minimizing this Euclidean distance should improve identity preservation in the reconstructed image.

• Image (a) is the original test images.
• Image (b) is reconstructed images generated using the initial latent approximations z0.
• Image (c) is reconstructed images generated using the “Pixelwise” and “Identity-Preserving” optimized latent approximations: z ∗ pixel and z ∗ IP.
• Image (d) is aging of the reconstructed images generated using the identity-preserving z ∗ IP latent approximations and conditioned on the respective age categories y (one per column).

Accompanied jupyter notebook for this post can be found on Github.

Conclusion

Age-cGANs can also use to build Face Aging system, Age synthesis and age progression have many practical industrial and consumer applications like cross-age face recognition, finding lost children, entertainment, visual effects in movies.

I hope this article provides a good explanation and understanding about Age-cGANs and it will help you get started building your own Age-cGANs.

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Published via Towards AI