An Efficient Approach to Correspondences between Multiple Non-Rigid Parts

Identifying multiple deformable parts on meshes and establishing dense correspondences between them are tasks of fundamental importance to computer graphics, with applications to e.g. geometric edit propagation and texture transfer. Much research has considered establishing correspondences between non-rigid surfaces, but little work can both identify similar multiple deformable parts and handle partial shape correspondences. This paper addresses two related problems, treating them as a whole: (i) identifying similar deformable parts on a mesh, related by a non-rigid transformation to a given query part, and (ii) establishing dense point correspondences automatically between such parts. We show that simple and efficient techniques can be developed if we make the assumption that these parts locally undergo isometric deformation. Our insight is that similar deformable parts are suggested by large clusters of point correspondences that are isometrically consistent. Once such parts are identified, dense point correspondences can be obtained by an iterative propagation process. Our techniques are applicable to models with arbitrary topology. Various examples demonstrate the effectiveness of our techniques

Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks

This paper presents an experiment designed to test the resilience of several user verification systems based on face recognition technology against simple identity spoofing methods, such as trying to gain access to the system by using mobile camera shots of the users, their ID cards, or social media photos of them that are available online. We also aim at identifying the compression threshold above which a photo can be used to gain access to the system. Four major user verification tools were tested: Keyemon and Luxand Blink on Windows and Android Face Unlock and FaceLock on Android. The results show all tested systems to be vulnerable to even very crude attacks, indicating that the technology is not ready yet for adoption in applications where security rather than user convenience is the main concern

A deep learning driven active framework for segmentation of large 3D shape collections

High-level shape understanding and technique evaluation on large repositories of 3D shapes often benefit from additional information known about the shapes. One example of such information is the semantic segmentation of a shape into functional or meaningful parts. Generating accurate segmentations with meaningful segment boundaries is, however, a costly process, typically requiring large amounts of user time to achieve high-quality results. In this paper we propose an active learning framework for large dataset segmentation, which iteratively provides the user with new predictions by training new models based on already segmented shapes. Our proposed pipeline consists of three components. First, we propose a fast and accurate feature-based deep learning model to provide dataset-wide segmentation predictions. Second, we develop an information theory measure to estimate the prediction quality and for ordering subsequent fast and meaningful shape selection. Our experiments show that such suggestive ordering helps to reduce users’ time and effort, produce high-quality predictions, and construct a model that generalizes well. Lastly, we provide interactive segmentation refinement tools, helping the user quickly correct any prediction errors. We show that our framework is more accurate and in general more efficient than the state-of-the-art for large dataset segmentation, while also providing consistent segment boundaries

Consistent segment-wise matching with multi-layer graphs

Segment-wise matching is an important problem for higher-level understanding of shapes and geometry analysis. Many existing segment-wise matching techniques assume perfect segmentation, and would suffer from imperfect or over-segmentation inputs. To handle this shortcoming, we propose a multi-layer graph (MLG) to represent possible partially merged segments of input shape. We adapt the diffusion pruning technique on the MLGs to find high quality segment-wise matching. Experimental results on man-made shapes demonstrate the effectiveness of our method

Consistent segment-wise matching with multi-layer graphs

Segment-wise matching is an important problem for higher-level understanding of shapes and geometry analysis. Many existing segment-wise matching techniques assume perfect segmentation, and would suffer from imperfect or over-segmentation inputs. To handle this shortcoming, we propose a multi-layer graph (MLG) to represent possible partially merged segments of input shape. We adapt the diffusion pruning technique on the MLGs to find high quality segment-wise matching. Experimental results on man-made shapes demonstrate the effectiveness of our method

Facial expression recognition in dynamic sequences: An integrated approach

Automatic facial expression analysis aims to analyse human facial expressions and classify them into discrete categories. Methods based on existing work are reliant on extracting information from video sequences and employ either some form of subjective thresholding of dynamic information or attempt to identify the particular individual frames in which the expected behaviour occurs. These methods are inefficient as they require either additional subjective information, tedious manual work or fail to take advantage of the information contained in the dynamic signature from facial movements for the task of expression recognition. In this paper, a novel framework is proposed for automatic facial expression analysis which extracts salient information from video sequences but does not rely on any subjective preprocessing or additional user-supplied information to select frames with peak expressions. The experimental framework demonstrates that the proposed method outperforms static expression recognition systems in terms of recognition rate. The approach does not rely on action units (AUs) and therefore, eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of AUs. The proposed framework explores a parametric space of over 300 dimensions and is tested with six state-of-the-art machine learning techniques. Such robust and extensive experimentation provides an important foundation for the assessment of the performance for future work. A further contribution of the paper is offered in the form of a user study. This was conducted in order to investigate the correlation between human cognitive systems and the proposed framework for the understanding of human emotion classification and the reliability of public databases