Age Label Distribution Learning Based on Unsupervised Comparisons of Faces

Although label distribution learning has made significant progress in the field of face age estimation, unsupervised learning has not been widely adopted and is still an important and challenging task. In this work, we propose an unsupervised contrastive label distribution learning method (UCLD) for facial age estimation. This method is helpful to extract semantic and meaningful information of raw faces with preserving high-order correlation between adjacent ages. Similar to the processing method of wireless sensor network, we designed the ConAge network with the contrast learning method. As a result, our model maximizes the similarity of positive samples by data enhancement and simultaneously pushes the clusters of negative samples apart. Compared to state-of-the-art methods, we achieve compelling results on the widely used benchmark, i.e., MORPH.

Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

This paper presents a deep learning approach for age estimation of human beings using their facial images. The different racial groups based on skin colour have been incorporated in the annotations of the images in the dataset, while ensuring an adequate distribution of subjects across the racial groups so as to achieve an accurate Automatic Facial Age Estimation (AFAE). The principle of transfer learning is applied to the ResNet50 Convolutional Neural Network (CNN) initially pretrained for the task of object classification and finetuning it’s hyperparameters to propose an AFAE system that can be used to automate ages of humans across multiple racial groups. The mean absolute error of 4.25 years is obtained at the end of the research which proved the effectiveness and superiority of the proposed method.

EXPRESS: Perceiving the facial trustworthiness: facial age, emotional expression and attractiveness

People can make trustworthiness judgements based on facial characteristics. However, the previous findings regarding on whether facial age influences interpersonal trust are inconsistent. Using the trust game, the current study investigated the interactions of facial age with attractiveness and emotional expression in regarding to trustworthiness judgements. In experiments 1 & 2, younger participants were asked to invest in either younger or older faces that were shown for 2000 ms and 33 ms respectively. The results showed that people trust the faces of older people more than they do of younger people. There was also an interaction between facial age and attractiveness. The participants invested more money in older faces than in younger faces only when they perceived the faces to be less attractive. However, the interaction between facial age and emotional expression was inconsistent in the two experiments. The participants invested more money in older faces that were shown for 2000 ms when they perceived the happy and sad emotions, but they invested more money in older faces that were shown for 33 ms when they perceived the happy emotion. These results reveal that people make trustworthiness judgements based on multiple facial cues when they view strangers of different ages.

A Multifeature Learning and Fusion Network for Facial Age Estimation

Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation.