Face Recognition via Multi-Level 3D-GAN Colorization
Zakir Khan, Arif Iqbal Umar, Syed Hamad Shirazi, Muhammad Shahzad, Muhammad Assam, M. T. El-Wakad, El-Awady Attia · IEEE Access · 2022
Rapid development in sketch-to-image translation methods boosts the investigation procedure in law enforcement agencies. But, the large modality gap between manually generated sketches makes this task challenging. Generative adversarial network (GAN) and encoder-decoder approach are usually incorporated to accomplish sketch-to-image generation with promising results. This paper targets the sketch-to-image translation with heterogeneous face angles and lighting effects using a multi-level conditional generative adversarial network. The proposed multi-level cGAN work in four different phases. Three independent cGANs’ networks are incorporated separately into each stage, followed by a CNN classifier. The Adam stochastic gradient descent mechanism was used for training with a learning rate of 0.0002 and momentum estimates β1 and β2 as 0.5 and 0.999, respectively. The multi-level 3D-convolutional architecture help to preserve spatial facial attributes and pixel-level details. The 3D convolution and deconvolution guide theG1,G2andG3to use additional features and attributes for encoding and decoding. This helps to preserve the direction, postures of targeted image attributes and special relationships among the whole image’s features. The proposed framework process the 3D-Convolution and 3D-Deconvolution using vectorization. This process takes the same time as 2D convolution but extracts more features and facial attributes. We used pre-trained ResNet-50, ResNet-101, and Mobile-Net to classify generated high-resolution images from sketches. We have also developed, and state-of-the-art Pakistani Politicians Face-sketch Dataset (PPFD) for experimental purposes. Result reveals that the proposed cGAN model’s framework outperforms with respect to Accuracy, Structural similarity index measure (SSIM), Signal to noise ratio (SNR), and Peak signal-to-noise ratio (PSNR).