关键词: 年龄评估, 计算机视觉, 特征提取, 有序回归, 面部特征

Abstract: Facial age estimation is an important task in the field of computer vision and is of great significance for applications such as face recognition and crowd analysis. Accurately extracting features from facial pictures is a critical and challenging task in age assessment. Traditional evaluation methods use ordered regression based on CNN, but when predicting adjacent ages, the method of convolutional neural network is difficult to capture the global feature representation, resulting in a decrease in prediction accuracy. In order to solve this problem, this paper proposes a new age assessment method that combines the improved Cloformer model with ordered regression. Compared with traditional ordered regression based on convolutional neural networks, Cloformer uses a self-attention mechanism to better capture the relationship between different regions in the image when capturing image features, so as to better learn the feature differences between adjacent ages. The method proposed in this article first optimizes the Cloformer model, and then combines the optimized Cloformer model with ordered regression to better utilize age sequence information and achieve more accurate age prediction, and then trains through end-to-end optimization The improved Cloformer model and ordered regression model enable the proposed method to better learn the relationship between facial features and age sequences. Finally, the superiority of the proposed method is verified by experiments on multiple public datasets.

Key words: facial age estimation, computer vision, feature extraction, ordinal regression, facial features