Social robots, also known as service or assistant robots, have been developed to improve the quality of human life in recent years. The design of socially capable and intelligent robots can vary, depending on the target user groups. In this work, we assess the effect of social robots' roles, functions, and communication approaches in the context of a social agent providing service or entertainment to users with developmental disabilities. In this paper, we describe an exploratory study of interface design for a social robot that assists people suffering from developmental disabilities. We developed series of prototypes and tested one in a user study that included three residents with various function levels. This entire study had been recorded for the following qualitative data analysis. Results show that each design factor played a different role in delivering information and in increasing engagement. We also note that some of the fundamental design principles that would work for ordinary users did not apply to our target user group. We conclude that social robots could benefit our target users, and acknowledge that these robots were not suitable for certain scenarios based on the feedback from our users.
As part of our robot GUI design, we first need to determine the manifestation of the virtual agent, if any. Since our robot has a physical presence already, instead of only being a pure personified character in a computer screen, we wanted to augment the embodiment of the graphic imagery in a physical semi-humanoid robot. @cite_15 argue that 3D face stimuli expedite children's recognition of facial expressions, based on an experiment on 71 children aged between 3 and 6. This study had been inspiring to our project, partly because of the closeness of IQ between our user groups. However, our tests of different portrayals of a given character did not show any evident disparity regarding users' acceptance, whether it was 2D or 3D.
@cite_15
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To enable real-time, person-independent 3D registration from 2D video, we developed a 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60 degrees. From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame. The algorithm utilizes a fast cascade regression framework trained on high-resolution 3D face-scans of posed and spontaneous emotion expression. The algorithm first estimates the location of a dense set of markers and their visibility, then reconstructs face shapes by fitting a part-based 3D model. Because no assumptions are required about illumination or surface properties, the method can be applied to a wide range of imaging conditions that include 2D video and uncalibrated multi-view video. The method has been validated in a battery of experiments that evaluate its precision of 3D reconstruction and extension to multi-view reconstruction. Experimental findings strongly support the validity of real-time, 3D registration and reconstruction from 2D video. The software is available online at http: zface.org. To enable real-time, person-independent 3D registration from 2D video, we developed a 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60. From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame. The algorithm utilizes a fast cascade regression framework trained on high-resolution 3D face-scans of posed and spontaneous emotion expression. The algorithm first estimates the location of a dense set of landmarks and their visibility, then reconstructs face shapes by fitting a part-based 3D model. Because no assumptions are required about illumination or surface properties, the method can be applied to a wide range of imaging conditions that include 2D video and uncalibrated multi-view video. The method has been validated in a battery of experiments that evaluate its precision of 3D reconstruction, extension to multi-view reconstruction, temporal integration for videos and 3D head-pose estimation. Experimental findings strongly support the validity of real-time, 3D registration and reconstruction from 2D video. The software is available online at http: zface.org. Display Omitted 3D cascade regression approach is proposed in which facial landmarks remain invariant.From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame.Multi-view reconstruction and temporal integration for videos are presented.Method is robust for 3D head-pose estimation under various conditions. This paper describes our initial work in developing a real-time audio-visual Chinese speech synthesizer with a 3D expressive avatar. The avatar model is parameterized according to the MPEG-4 facial animation standard [1]. This standard offers a compact set of facial animation parameters (FAPs) and feature points (FPs) to enable realization of 20 Chinese visemes and 7 facial expressions (i.e. 27 target facial configurations). The Xface [2] open source toolkit enables us to define the influence zone for each FP and the deformation function that relates them. Hence we can easily animate a large number of coordinates in the 3D model by specifying values for a small set of FAPs and their FPs. FAP values for 27 target facial configurations were estimated from available corpora. We extended the dominance blending approach to effect animations for coarticulated visemes superposed with expression changes. We selected six sentiment-carrying text messages and synthesized expressive visual speech (for all expressions, in randomized order) with neutral audio speech. A perceptual experiment involving 11 subjects shows that they can identify the facial expression that matches the text message’s sentiment 85 of the time. Even if, most of 2D face recognition approaches reached recognition rate more than 90 in controlled environment, current days face recognition systems degrade their performance in case of uncontrolled environment which includes pose variations, illumination variations, expression variations and ageing effect etc. Inclusion of 3D face analysis gives an age over 2D face recognition as they give vital informations such as 3D shape, texture and depth which improve discrimination power of an algorithm. In this paper, we have investigated different 3D face recognition approaches that are robust to changes in facial expressions and illumination variations. 2D-PCA and 2D-LDA approaches have been extended to 3D face recognition because they can directly work on 2D depth image matrices rather than 1D vectors without need for transformations before feature extraction. In turn, this reduces storage space and time required for computations. 2D depth image is extracted from 3D face model and nose region from depth mapped image has been detected as a reference point for cropping stage to convert model into a standard size. Two Dimensional Principal Component Analysis (2D-PCA) and Two Dimensional Linear Discriminant analysis (2D-LDA) are employed to obtain feature vectors globally compared to feature vectors obtained locally using PCA or LDA. Finally, euclidean distance classifier is applied for comparison of extracted features. A set of experiments on GavabDB 3D face database, which has 61 individuals in total, demonstrated that 3D face recognition using 2D-LDA method has achieved recognition accuracy of 93.3 and EER of 8.96 over database, which is higher compared to 2D-PCA. So, more optimized performance has been achieved using 2D-LDA for 3D face recognition analysis.