Toward Quantifiable Face age Transformation

Face aging is a highly complex process that includes intricate aging patterns. Previous works condition aging patterns utilizing one-hot or artificial predefined distributions. Nevertheless, different age groups show different intraclass variation in appearance. This makes it difficult for previous methods to discriminately express differences in apparent age across all age groups leading to degradation of model performance. To address this issue, we propose the Shapley Value Quanti-zation(SVQ) module and the Differentiated Age Embedding Transformation(DAT) module for calculating age differences and performing age modulation. Specifically, the SVQ module quantifies the contribution of different attributes to age using Shapley values. This allows us to obtain adaptive age distributions for different age groups. Subsequently, the DAT module takes a target age vector, sampled from the target age distribution quantized by SVQ, and modulates the age representation of the generated image. Experimental results show outperforms our approach in comparison to the state of the arts face aging methods by a large margin.