Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, humancomputer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MASFV2: Mixed AttentionShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the stateoftheart.

Efficient convolutional neural network for apparent age prediction This paper proposes an efficient convolutional neural network architecture for apparent age estimation from single face image capable of top performance without the use of a pretraining stage. The architecture consists of 9 convolution layers and 2 max pool layers and requires only 79k parameters. We also improve the results by applying a weighted class distribution which assures that overwhelmingly represented classes do not skew the prediction results. These are compared to other results in literature for both methods that use pretraining and those that do not. The proposed method achieves estimation errors comparable to the human reference and to existing methods while being characterized by efficiency at both training and testing, it employs orders of magnitude fewer parameters and much less training time than other stateoftheart methods.