In recent years, the research on age estimation based on face images has drawn more and more attention, which includes two processes: feature extraction and estimation function learning. In the aspect of face feature extraction, this paper leverages excellent characteristics of convolution neural network in the field of image application, by using deep learning method to extract face features, and adopts factor analysis model to extract robust features. In terms of age estimation function learning, age-based and sequential study of rank-based age estimation learning methods is utilized and then a divide-and-rule face age estimator is proposed. Experiments in FG-NET, MORPH Album 2, and IMDB-WIKI show that the feature extraction method is more robust than traditional age feature extraction method and the performance of divide-and-rule estimator is superior to classical SVM and SVR.

Time-series forecasting (TSF) is becoming ubiquitous in numerous real-world applications. The loss function is a vital component of TSF models based on deep learning. The widely used $L_{p}$ -norm distance-based loss functions, which are vulnerable and biased to not consider dynamic temporal patterns and label autocorrelation, resulting in their inability to capture the semantics or shape of the sequence well. In practice, the sequence data will have various distortions due to the influence of environmental factors, and how to overcome the data distortion variations is an important challenge for loss function design in TSF. Aiming at the above challenges, a loss function framework based on semantic-aware learning was designed, which consists of global trend direction guidance and point-wise local trend terms. Based on the loss function framework, we propose a novel loss function, called Tre-Loss (semantic-aware trend learning-based loss function), that not only considers the distortions in all aspects but also allows models to capture the semantics of time series. Tre-Loss is a plug-and-play loss function that can be directly applied to almost arbitrary TSF network. Evaluate the effectiveness of Tre-Loss by conducting extensive experiments from naive models to state-of-the-art models. The experimental results indicate that the models trained with Tre-Loss outperform those trained with other training metrics. The code and models will be available on GitHub.

Automated facial gender and age classification has remained a challenge because of the high inter-subject and intra-subject variations. We addressed this challenging problem by studying multi-instance- and multi-scale-enhanced multi-task random forest architecture. Different from the conventional single facial attribute recognition method, we designed effective multi-task architecture to learn gender and age simultaneously and used the dependency between gender and age to improve its recognition accuracy. In the study, we found that face gender has a great influence on face age grouping; thus, we proposed a random forest face age grouping method based on face gender conditions. Specifically, we first extracted robust multi-instance and multi-scale features to reduce the influence of various intra-subject distortion types, such as low image resolution, illumination and occlusion, etc. Furthermore, we used a random forest classifier to recognize facial gender. Finally, a gender conditional random forest was proposed for age grouping to address inter-subject variations. Experiments were conducted by using two popular MORPH-II and Adience datasets. The experimental results showed that the gender and age recognition rates in our method can reach 99.6% and 96.14% in the MORPH-II database and 93.48% and 63.72% in the Adience database, reaching the state-of-the-art level.