How a FACE APP is working using Deep Learning

Face Aging Using Conditional GANs (GANs) 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 GANs to perform automatic face aging. Face Aging GAN(Age-GANs) introduced by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay, in their paper with titled Face Aging With Conditional Generative Adversarial Networks. The Face Aging-Gan 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.

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