Facial analysis has significant applications in human-computer interaction and multimedia communication research. In this paper, we aim to investigate the impact of facial regions and features in the facial analysis tasks. In particular, we compare the performances of various facial features extracted from different regions in two specific tasks, namely, age group prediction and twin recognition. Furthermore, we also evaluate the fusion of handcrafted features and deep learning features in both tasks. Through the experiments, we highlight different impacts of facial regions and facial features combination in facial analysis tasks.

In this work, we aim to introduce our framework that performs the hand shadow puppetry using robotic arms. Human arms are not flexible and dynamic enough to produce many complicated shadow poses so we aim to utilize robotic arms for shadow puppetry. Firstly, we construct a shadow library to cover all the possible shadow images for a single robotic hand. For the input image, we first extract the shadow image by using a salient object detector. Then we match the shape correspondences between the input shadow image with the ones in the shadow library. Finally, we transfer the corresponding parameters of the best matching shadow in the library into proper utility to control the physical robotics arms.