This paper proposes a robust framework for reconstructing 2D human facial images from half-frontal views, primarily captured under low-quality surveillance conditions. A custom MATLAB-based Graphical User Interface (GUI) is developed to support the complete pipeline, including frame extraction, enhancement, and face reconstruction. Representative frames are extracted and enhanced for video inputs using one of three techniques: histogram equalization, contrast stretching, or logarithmic transformation. Reconstruction involves detecting a single eye from the half-frontal image, followed by horizontal flipping and concatenation to generate a symmetric full-frontal face. The reconstructed faces are validated using the Viola-Jones object detection algorithm to confirm the presence and alignment of facial features. Quantitative evaluation uses the Structural Similarity Index (SSIM) and Jaccard Index (JI) to measure image quality and geometric accuracy. The proposed method is tested on publicly available datasets and a custom-designed dataset reflecting real-world surveillance challenges such as low resolution and poor illumination. Experimental results demonstrate that the framework delivers accurate and visually coherent reconstructions with low computational overhead, making it suitable for real-time surveillance and facial analysis applications.