Head pose estimation on depth data based on Particle Swarm Optimization

This paper presents a joint head pose and facial landmark regression method with input from depth images for realtime application. Our main contributions are: firstly, a joint optimization method to estimate head pose and facial landmarks, i.e., the pose regression result provides supervised initialization for cascaded facial landmark regression, while the regression result for the facial landmarks can also help to further refine the head pose at each stage. Secondly, we classify the head pose space into 9 sub-spaces, and then use a cascaded random forest with a global shape constraint for training facial landmarks in each specific space. This classification-guided method can effectively handle the problem of large pose changes and occlusion. Lastly, we have built a 3D face database containing 73 subjects, each with 14 expressions in various head poses. Experiments on challenging databases show our method achieves state-of-the-art performance on both head pose estimation and facial landmark regression.

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“…5 Cascaded facial landmark localization. Starting from the initial guess, the locations of the landmarks are gradually optimized.…”

Section: Figurementioning

confidence: 99%

“…These two problems have been separately studied for many years [3][4][5][6][7], with significant progress for images [8][9][10][11]. However, image-based methods are always subject to illumination and pose angle variations [12], which lead to many limitations.…”

Section: Introductionmentioning

confidence: 99%

Joint head pose and facial landmark regression from depth images

et al. 2017

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“…(4)) is performed based on Particle Swarm Optimization (PSO) [19] which is a stochastic, evolutionary optimization method. It has been demonstrated that PSO is a very effective and efficient method for solving other vision optimization problems such as head pose estimation [23], hand articulation tracking [22] and others. PSO achieves optimization based on the collective behavior of a set of particles (candidate solutions) that evolve in runs called generations.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Panteleris

Argyros

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.

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“…Accordingly, it has 1 A borrowing from Latin word 'ludus' (game), combined with an English element; The term has historically been used to describe the study of games. received extensive coverage in the scientific literature and a variety of techniques have been reported for precisely compute head pose [3], while depth information can also be integrated [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

Kalliatakis¹,

Stergiou²,

Vidakis³

2017

Preprint

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Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player's training and ludology 1 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 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. Motivated by the need of generating 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).

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Head pose estimation on depth data based on Particle Swarm Optimization

Cited by 74 publications

References 26 publications

Joint head pose and facial landmark regression from depth images

Joint head pose and facial landmark regression from depth images

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Bringing Depth Data Alive: Conceive Human Intention through Web Visualisations of Head Pose and Emotion Changes <sup>&dagger;</sup>

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