Suspect Face Generation
Harsh Jaykumar Jalan, Gautam Maurya, Canute Corda, Sunny Dsouza, Dakshata Panchal · 2020
Currently sketch artists are employed by the police to draw sketches of suspects based on the description given by an eye-witness. These sketches can sometimes be inaccurate due to incorrect drawings of the artist or the incorrect description given by the witness. Generative Adversarial Network (GAN) is a way of training a Neural Network to output images which belong to a specific class. This network is trained by using an adversarial process which pits the generator against the discriminator in a minimax game. Traditional GANs are unable to generate high-resolution images hence, StyleGAN is used to resolve this issue. The generated images may still need to be altered to get a close match so TL-GAN is used to alter the generated image by altering the latent-space input of the StyleGAN. TL-GAN offers users the ability to finely tune one or multiple features of the face holistically. The main objective of the proposed work is to develop a Suspect Face Generation System as the sketches made by sketch artists are only 13 out of 160 times (approx. 8%) accurate. This system will help the society in reduction of misidentification of crime suspects and considerably reduce the crime rate.