Privacy-preserving & Emotional Understanding of Human Faces based on Machine Learning Techniques

With the development of technology, the human face could be used as a key to accessing critical resources. Understanding the potentiality of the human face is crucial before using unique facial features in the face application. As such, the novel Local Directional Texture (LDT) and its variants, temporal strips (TS) and action unit temporal blocks (AU-TB), are proposed for privacy-preserving face recognition. The standard datasets are also analysed to revisit the action units (AUs) encoded in the dataset ground truths and inspect whether any significant AU has been left out. Lastly, a graph-based algorithm is developed to classify the micro-expressions.