The Technology of Generating Facial Expressions for Film and Television Characters Based on Deep Learning Algorithms

This article presents a deep learning approach that integrates Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) to enhance the generation of natural and realistic facial expressions for film and television characters. The model leverages CNN for feature extraction from facial images, feeding into a GAN generator to produce highly realistic expressions. Hyperparameter tuning, including learning rate, batch size, and Dropout ratio, optimizes model performance. Experiments demonstrate substantial improvements in PSNR (from 30.43 dB to 45.95 dB) and SSIM, indicating enhanced image quality. The CNN-GAN model also excels in naturalness, diversity, and coherence of generated expressions, achieving a comprehensive score of 0.947, marking a significant advancement in static image expression generation for digital media and entertainment.