Research on a face data processing method based on sparse autoencoder neural networks

When confronted with recognizing large facial images, traditional feature extraction methods encounter significant difficulties in extracting compelling features, leading to low accuracy in face recognition. This paper proposes a face data processing method based on sparse neural networks to address this issue. This method adopts a comprehensive technical framework utilizing the algorithm of sparse autoencoder neural networks. Specifically, the technique integrates Gibbs sampling-based generative adversarial networks, physical models (such as illumination models), improved autoencoder algorithms, and deterministic return-based extreme learning machine algorithms. Through the synergistic effect of these methods, we achieve efficient processing, feature extraction, and classification of face images, thereby enhancing the accuracy and efficiency of face data processing.