Statistical Machine Learning for Human Behaviour Analysis

Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition. This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue [1-15]. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field. Most of the included papers are application-based systems, while [15] focuses on the understanding and interpretation of a classification model, which is an important factor for the classifier's credibility. Given a set of categorical data, [15] utilizes multi-objective optimization algorithms, like ENORA and NSGA-II, to produce rule-based classification models that are easy to interpret. Performance of the classifier and its number of rules are optimized during the learning, where the first one is obviously expected to bemaximizedwhile the second one is expected to beminimized. Testing on public databases, using 10-fold cross-validation, shows the superiority of the proposed method against classifiers that are generated using other previously published methods like PART, JRip, OneR and ZeroR. Two published papers ([1,9]) have privacy as their main concern, while they develop their respective systems for biometrics recognition and action recognition. Reference [1] has considered a privacy-aware biometrics system. The idea is that the identity of the users should not be readily revealed from their biometrics, like facial images. Therefore, they have collected a database of foot and hand traits of users while opening a door to grant or deny access, while [9] develops a privacy-aware method for action recognition using recurrent neural networks. The system accumulates reflections of light pulses omitted by a laser, using a single-pixel hybrid photodetector. This includes information about the distance of the objects to the capturing device and their shapes