METHOD OF MODIFIED POSE-INVARIANT FACE FRONTALIZATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS WITH L1–L2 LOSS REGULARIZATION

Pose variation in facial imagery presents a persistent challenge for automated face recognition systems, particularly in uncontrolled environments such as surveillance, access control, and mobile device authentication. This paper introduces an approach based on Conditional Generative Adversarial Network (cGAN) for synthesizing photorealistic frontal views from single profile images. The proposed architecture concatenates a spatially replicated noise vector with the input profile, enabling generation diversity while retaining subject identity. A composite loss function integrating adversarial, L1, and L2 losses is employed to enhance both global realism and pixel-level fidelity. The model is trained on a custom dataset comprising 4,682 images of 44 subjects, each with a single frontal view and multiple side profiles. Training is performed incrementally to improve stability and convergence. Qualitative results indicate that the method produces visually convincing frontal images with preserved identity details. This work establishes a foundation for future extensions involving perceptual loss, identity-preserving regularization, and large-scale evaluations.