Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood for applications in education, healthcare, and entertainment, among others. Deep convolutional neural networks show promising results in classifying facial expressions of adults. However, classifier models trained with adult benchmark data are unsuitable for learning child expressions due to discrepancies in psychophysical development. Similarly, models trained with child data perform poorly in adult expression classification. We propose domain adaptation to concurrently align distributions of adult and child expressions in a shared latent space for robust classification of either domain. Furthermore, age variations in facial images are studied in age-invariant face recognition yet remain unleveraged in adult-child expression classification. We take inspiration from multiple fields and propose deep adaptive FACial Expressions fusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child expression classification. For the first time in the literature, a mixture of Beta distributions is used to decompose and select facial features based on correlations with expression, domain, and identity factors. We evaluate FACE-BE-SELF using 5-fold cross validation for two pairs of adult-child data sets. Our proposed FACE-BE-SELF approach outperforms transfer learning and other baseline domain adaptation methods in aligning latent representations of adult and child expressions.

As edge computing devices for Internet of Things (IoT) applications become more capable, the demand for data driven deep neural network (DNN) based algorithms grows. However, this leads to increased model vulnerability to adversarial attack. The Visual Internet of Things (VIoT), a subdomain of IoT, has great need for methods to improve robustness of algorithms to such attacks, along with person Re-Identification (Re-ID) through 3-dimensional (3-D) skeleton-based gait-analysis. For other domains, either adversarial training or synthetic data augmentation has shown to improve model performance in benign and adversarial attack cases. However, a combined approach that leverages both for robust classification in IoT has yet to be studied for 3-D skeleton based Re-ID. Additionally, assessment of 3-D skeleton-based Re-ID model vulnerability to adversarial attacks along with methods to improve robustness against such attacks are critical literature gaps. This work seeks to address these gaps by proposing an adversarial attack tolerant computational framework for 3-D skeleton-based Re-ID. Our approach, for the first time in the literature, leverages both synthetic data augmentation using a Generative Adversarial Network (GAN) and adversarial training through transfer learning of 3-D skeleton data on a Convolutional Neural Network (CNN) for person Re-ID. We evaluate the robustness of our method against seven popular adversarial attacks across four unique Re-ID 3-D skeleton datasets. We demonstrate how our method responds to varying amounts of real data and study the viability of our method for deployment on edge devices. Our findings show the proposed approach improves test accuracy in both the benign and adversarial case. Our method is deployable to small and inexpensive edge devices popular in IoT like the Raspberry Pi microcomputer.