Looking at People Special Issue

The automatic analysis of humans in static images and video sequences, known as Looking at People, keeps making rapid progress with the regular improvement of existing methods and the proposal of new paradigms that constantly push the state-of-the-art. Applications are countless, including human computer interaction, affective computing, human robot interaction, communication, entertainment, security, commerce and sports, while having an important social impact in assistive technologies for the handicapped and the elderly. Because of this overwhelming spectrum of development and applications, we edited this special issue that aims at covering all aspects of Looking at People. This compendium is a natural follow up of a series of events organized on this topic by ChaLearn Looking at People,1 which focuses on challenge organization in the fields of computer vision and machine learning (Baró et al. 2015; Escalera et al. in “ChaLearn Looking at People: A Review of Events and Resources", https://doi.org/10.1109/IJCNN. 2017.7966041; Escalera et al. 2017). During the last 7 years ChaLearn has witnessed an important evolution in LAP methodologies, in essence thanks to the use of depth sensors, the collection and annotation of large databases, the empowerment of deep neural networks, and their huge success in multiple fields like social science. Consequently, the characterization of human behavior in image sequences has become one of the most important research topics in areas