Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data

Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Such mechanism can yield a platform for objective and effortless assessment of human activity within the context of serious gaming and human-computer interaction.Comment: 8th Computer Science and Electronic Engineering, (CEEC 2016), University of Essex, UK, 6 page

Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our tasks. In our system we employ effective depth map processing techniques, along with edge detection, connected components detection and filtering approaches, in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects. Besides, we apply the Linear Spatial Pyramid Matching (LSPM) [1] method proposed by Jianchao Yang et al for the efficient classification of the detected objects. Experimental results are presented for both detection and classification, showing the efficiency of the proposed design

EduARdo—Unity Components for Augmented Reality Environments

Contemporary software applications have shifted focus from 2D representations to 3D. Augmented and Virtual Reality (AR/VR) are two technologies that have captured the industry’s interest as they show great potential in many areas. This paper proposes a system that allows developers to create applications in AR and VR with a simple visual process, while also enabling all the powerful features provided by the Unity 3D game engine. The current system comprises two tools, one for the interaction and one for the behavioral configuration of 3D objects within the environment. Participants from different disciplines with a software-engineering background were asked to participate in the evaluation of the system. They were called to complete two tasks using their mobile phones and then answer a usability questionnaire to reflect on their experience using the system. The results (a) showed that the system is easy to use but still lacks some features, (b) provided insights on what educators seek from digital tools to assist them in the classroom, and (c) that educators often request a more whimsical UI as they want to use the system together with the learners

Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions

Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player’s training and ludology experience in serious games, or analysis of customer satisfaction regarding broadcast and web services, or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests were selected in order to rapidly and accurately estimate head pose changes in an unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation (JSON)) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need to generate comprehensible visual representations from different sets of data, in this paper, we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor)

Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions

Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player’s training and ludology experience in serious games, or analysis of customer satisfaction regarding broadcast and web services, or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests were selected in order to rapidly and accurately estimate head pose changes in an unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation (JSON)) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need to generate comprehensible visual representations from different sets of data, in this paper, we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor)