Improved facial feature fitting for model based coding and animation

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Accurate Corner Detection Methods using Two Step Approach

Many image features are proved to be good candidates for recognition. Among them are edges, lines, corners, junctions or interest points in general. Importance of corner detection in digital images is increasing with increasing work in computer vision in imagery. One of the most promising techniques is the one based on Harris corner detection method. This work describes different approaches to detect corner in efficient way. Based on the works carried out by Harris method, the authors have worked upon increasing efficiency using edge detection methods on image, along with applying the Harris on this pre-processed image. Most of the time, such a step is performed as one of the first steps upon which more complicated algorithm rely. Hence, good outcome of such an operation influences the whole vision channel. This paper contains a quantitative comparison of three such modified techniques using Sobel2013;Harris, Canny-Harris and Laplace-Harris with Harris operator on the basis of distances computed by these methods from user detected corners

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Hierarchical eyelid and face tracking

Most applications on Human Computer Interaction (HCI) require to extract the movements of user faces, while avoiding high memory and time expenses. Moreover, HCI systems usually use low-cost cameras, while current face tracking techniques strongly depend on the image resolution. In this paper, we tackle the problem of eyelid tracking by using Appearance-Based Models, thus achieving accurate estimations of the movements of the eyelids, while avoiding cues, which require high-resolution faces, such as edge detectors or colour information. Consequently, we can track the fast and spontaneous movements of the eyelids, a very hard task due to the small resolution of the eye regions. Subsequently, we combine the results of eyelid tracking with the estimations of other facial features, such as the eyebrows and the lips. As a result, a hierarchical tracking framework is obtained: we demonstrate that combining two appearance-based trackers allows to get accurate estimates for the eyelid, eyebrows, lips and also the 3D head pose by using low-cost video cameras and in real-time. Therefore, our approach is shown suitable to be used for further facial-expression analysis.Peer Reviewe

Semantic Analysis of Facial Gestures from Video Using a Bayesian Framework

The continuous growth of video technology has resulted in increased research into the semantic analysis of video. The multimodal property of the video has made this task very complex. The objective of this thesis was to research, implement and examine the underlying methods and concepts of semantic analysis of videos and improve upon the state of the art in automated emotion recognition by using semantic knowledge in the form of Bayesian inference. The main domain of analysis is facial emotion recognition from video, including both visual and vocal aspects of facial gestures. The goal is to determine if an expression on a person\u27s face in a sequence of video frames is happy, sad, angry, fearful or disgusted. A Bayesian network classification algorithm was designed and used to identify and understand facial expressions in video. The Bayesian network is an attractive choice because it provides a probabilistic environment and gives information about uncertainty from knowledge about the domain. This research contributes to current knowledge in two ways: by providing a novel algorithm that uses edge differences to extract keyframes in video and facial features from the keyframe, and by testing the hypothesis that combining two modalities (vision with speech) yields a better classification result (low false positive rate and high true positive rate) than either modality used alone