Abstract:

The use of technology in the last decades has been shooting up to the sky. Everything is interlinked with technology. As the world is moving faster towards technology, humankind is also changing their regular needs with technology. The technology involved in the processing of the image is been extremely popular. Image processing is one of the innovative and creative methods to deal with the amazing outcome of the images. This project involves the technology of image processing, in which deep learning is being used. There has been a major problem in predicting the age of a person just by looking at them. The technology is so developed, that we can do anything using it. This project not only predicts the age of a person but also can-do age progression on the image that is, we can see how the child will age and how the old looks in his child’s age. The use and development of this project are by machine learning algorithms and models. A subfield of artificial intelligence and computer science called "machine learning" focuses on using data and algorithms to mimic how people learn, gradually increasing the accuracy of its predictions. A key element of the developing discipline of data science is machine learning. GANs of the machine learning model is used in this project. Generative Adversarial Networks are a method of generative modelling that creates new data from training data that closely resembles training data. Two competing core blocks in GANs are able to copy, collect, and analyse changes in a dataset. Typically, the two models go by the names Generator and Discriminator. In this project CycleGANs are used, like in GANs similarly in CycleGANs there are two generator and discriminator models.

Description:

Age Prediction, Age Progression, Age Regression, CNN, CycleGAN, GAN