ABSTRACT
The reconstruction of the face has historically been a significant issue in medical and forensic science. The presence of COVID-19 has added a significant new dimension. To model a new face, plastic surgery and informatics are employed, representing cyber forensics with challenges. The classic facial recognition techniques suffer from major drawbacks when face masks are widely used. As a result, new techniques are now being tried and tested to reconstruct a face from a collection of masked facial images. To determine the identification accuracy and other parameters/metrics, these faces are compared to real-world images of the same subject. Our research focuses on the task of post-mask face reconstruction, addressing the pressing need for precise and reliable techniques. We evaluate the effectiveness of three key algorithms: Edge Connect, Gated Convolution, and Hierarchical Variational Vector Quantized Autoencoders (HVQVAE). We use two synthetic datasets, MaskedFace-CelebA and MaskedFace-CelebAHQ, to rigorously assess the quality of reconstructed faces using metrics such as PSNR, SSIM, UIQI, and NCORR. Gated Convolution (GC) emerges as the superior choice in terms of image quality. To validate our findings, we employ five classifiers (Vgg16, Vgg19, ResNet50, ResNet101, ResNET152) and explore Extreme Learning Machine (ELM) and Support Vector Machine (SVM) as novel approaches for face recognition. A comprehensive ablation study reinforces our conclusion that Generative Convolution (GC) excels among the three models. Our research offers valuable insights into face reconstruction amid widespread mask usage, emphasizing innovative methodologies to address contemporary challenges in the field.