Comparative Convolutional Neural Network for Younger Face Identification We consider the problem of determining whether a pair of face images can be distinguishable in terms of age and if so, which is the younger of the two. We also determine the degree of distinguishability in which age differences are categorized into large, medium, small and tiny. We propose a comparative convolutional neural network combining two parallel deep architectures. Based on the two deep learnt face features, we introduce a comparative layer to represent their mutual relationships, followed by a concatenatation implementation. Softmax is adopted to complete the classification task. To demonstrate our approach, we construct a very large dataset consisting of over 1.7 million face image pairs with youngold labels.

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.