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Über dieses Buch

This book constitutes the refereed proceedings of the 6th International Workshop on Representations, Analysis and Recognition of Shape and Motion from Imaging Data, RFMI 2016, held in Sidi Bou Said Village, Tunisia, in October 2016.

The 9 revised full papers and 7 revised short papers presented were carefully reviewed and selected from 23 submissions. The papers are organized in topical sections on 3D shape registration and comparison; face analysis and recognition; video and motion analysis; 2D shape analysis.



3D Shape Registration and Comparison


Local Feature-Based 3D Canonical Form

In this paper, we present a novel approach to compute 3D canonical forms which is useful for non-rigid 3D shape retrieval. We resort to using the feature space to get a compact representation of points in a small-dimensional Euclidean space. Our aim is to improve the classical Multi-Dimensional Scaling MDS algorithm to avoid the super-quadratic computational complexity. To this end, we compute the canonical form of the local geodesic distance matrix between pairs of a small subset of vertices in local feature patches. To preserve local shape details, we drive the mesh deformation by the local weighted commute time. When used as a spatial relationship between local features, the invariant properties of the Biharmonic distance improve the final results.We evaluate the performance of our method by using two different measures: the compactness measure and the Haussdorf distance.

Hela Haj Mohamed, Samir Belaid, Wady Naanaa

Accurate 3D Shape Correspondence by a Local Description Darcyan Principal Curvature Fields

In this paper, we propose a novel approach for finding correspondence between three-dimensional shapes undergoing non-rigid transformations. Our proposal is based on the computation of the mean of curvature fields values on a local parametrization constructed around interest points on the surface. This local parametrization corresponds to the Darcyan coordinates system. Thereafter, correspondence is found by measuring the $$L_{2}$$ distance between obtained descriptors. We conduct the experimentation on the full objects of the Tosca database which contains a set of 3D objects with non-rigid deformations. The obtained results show the performance of the proposed approach.

Ilhem Sboui, Majdi Jribi, Faouzi Ghorbel

A Novel Robust Statistical Watermarking of 3D Meshes

In this paper, we present a novel robust blind 3D mesh watermarking approach. We embed signature bits into the vertex norms distribution. At first, the robust source locations are extracted by using a salient point detector, based on the Auto Diffusion Function (ADF). Afterwards, the mesh is segmented into different regions according to the detected salient points. Then, the same watermark bits are embedded statistically into each region. The experimental results show the robustness of our method against cropping and other common attacks. Due to the stability of salient points, we can retrieve the watermarked region and extract the watermark. In addition, the performance of our method is also demonstrated on the minimal surface distortion in the embedding process.

Nassima Medimegh, Samir Belaid, Mohamed Atri, Naoufel Werghi

Face Analysis and Recognition


Shape Analysis Based Anti-spoofing 3D Face Recognition with Mask Attacks

With the growth of face recognition, the spoofing mask attacks attract more attention in biometrics research area. In recent years, the countermeasures based on the texture and depth image against spoofing mask attacks have been reported, but the research based on 3D meshed sample has not been studied yet. In this paper, we propose to apply 3D shape analysis based on principal curvature measures to describe the meshed facial surface. Meanwhile, a verification protocol based on this feature descriptor is designed to verify person identity and to evaluate the anti-spoofing performance on Morpho database. Furthermore, for simulating a real-life testing scenario, FRGCv2 database is enrolled as an extension of face scans to augment the ratio of genuine face samples to fraud mask samples. The experimental results show that our system can guarantee a high verification rate for genuine faces and the satisfactory anti-spoofing performance against spoofing mask attacks in parallel.

Yinhang Tang, Liming Chen

Early Features Fusion over 3D Face for Face Recognition

In this paper, a novel approach for fusing shape and texture Local Binary Patterns (LBP) for 3D Face Recognition is presented. Using the recently proposed mesh-LBP [23], it is now possible to compute LBP directly on a mesh manifold, allowing Early Feature Fusion to enhance face description power. Compared to its depth image counterparts, the proposed method (a) inherits the intrinsic advantages of mesh surfaces, (such as preservation of full geometry), (b) does not require face registration, (c) can accommodate partial or rotation matching, and (d) natively allows early-level fusion of texture and shape descriptors. The advantages of early-fusion is presented together with an experimentation of two merging schemes tested on the Bosphorus database.

Claudio Tortorici, Naoufel Werghi

Enhancing 3D Face Recognition by a Robust Version of ICP Based on the Three Polar Representation

In this paper, we intend to propose a framework for the description and the matching of three dimensional faces. Our starting point is the representation of the 3D face by an invariant description under the M(3) group of translations and rotations. This representation is materialized by the points of the arc-length reparametrization of all the level curves of the three polar representation. These points are indexed by their level curve number and their position in each level. With this type of description we need a step of registration to align 3D faces with different expressions. Therefore, we propose to use a robust version of the iterative closest point algorithm (ICP) adopted to 3D face recognition context. We test the accuracy of our approach on a part of the BU-3DFE database of 3D faces. The obtained results for many protocols of the identification scenario show the performance of such framework.

Amal Rihani, Majdi Jribi, Faouzi Ghorbel

3D Nasal Shape: A New Basis for Soft-Biometrics Recognition

In the past 10 years, Soft-Biometrics recognition using 3D face has become prevailing, with many successful research works developed. In contrast, the usage of facial parts for Soft-Biometrics recognition remains less investigated. In particular, the nasal shape contains rich information for demographic perception. They are usually free from hair/glasses occlusions, and stay robust to facial expressions, which are challenging issues 3D face analysis. In this work, we propose the idea of 3D nasal Soft-Biometrics recognition. To this end, the simple 3D coordinates features are derived from the radial curves representation of the 3D nasal shape. With the 466 earliest scans of FRGCv2 dataset (mainly neutral), we achieved 91% gender (Male/Female) and 94% ethnicity (Asian/Non-asian) classification rates in 10-fold cross-validation. It demonstrates the richness of the nasal shape in presenting the two Soft-Biometrics, and the effectiveness of the proposed recognition scheme. The performances are further confirmed by more rigorous cross-dataset experiments, which also demonstrates the generalization ability of propose approach. When experimenting on the whole FRGCv2 dataset (40% are expressive), comparable recognition performances are achieved, which confirms the general knowledge that the nasal shape stays robust during facial expressions.

Baiqiang Xia

Towards a Methodology for Retrieving Suspects Using 3D Facial Descriptors

We propose a first investigation towards a methodology for exploiting 3D descriptors in suspect retrieval in the context of crime investigation. In this field, the standard method is to construct a facial composite, based on witness description, by an artist of via software, then search a match for it in legal databases. An alternative or complementary scheme would be to define a system of 3D facial attributes that can fit human verbal face description and use them to annotate face databases. Such framework allows a more efficient search of legal face database and more effective suspect shortlisting. In this paper, we describe some first steps towards that goal, whereby we define some novel 3D face attributes, we analyze their capacity for face categorization though a hieratical clustering analysis. Then we present some experiments, using a cohort of 107 subjects, assessing the extent to which some faces partition based on some of these attributes meets its human-based counterpart. Both the clustering analysis and the experiments results reveal encouraging indicators for this novel proposed scheme.

Naoufel Werghi, Hassen Drira

Video and Motion Analysis


Key Frame Selection for Multi-shot Person Re-identification

Typical person re-identification approaches rely on a single image to model the visual appearance characteristics for each target. The performance of these systems is very limited as they ignore the immense amount of video data produced by the practical surveillance systems. In this paper, we present a novel multi-shot person re-identification approach based on key frame selection. We propose to conduct a global appearance signature by automatically selecting a set of representative appearance images depicting the different body postures from the target’s trajectory. Then, these images will be modeled into a global appearance signature to perform the re-identification task based on set matching strategy. The robustness of our approach is validated on the challenging HDA+ dataset in contrast to the limitations of existing approaches.

Mayssa Frikha, Omayma Chebbi, Emna Fendri, Mohamed Hammami

An Exact Smoother in a Fuzzy Jump Markov Switching Model

In this paper, we proposed an extension of the classical Conditionally Gaussian Observed Markov Switching Model (CGOMSM) by incorporating fuzzy switches. The proposed approach allows the modeling of transient switches and handles the discontinuity feature in switching regime models by using fuzzy switches instead of hard jumps. Fuzzy switched based approach is more adapted to real-world application in which regime continuity is an intrinsic property. To define an efficient scheme for an exact smoothing in CGOMFSM, we adapt fast smoothing equations to cope with the fuzzy model. Finally, we show through several experiments the interest of the fuzzy switches model.

Zied Bouyahia, Stéphane Derrode, Wojciech Pieczynski

2D Shape Analysis


A Novel 2D Contour Description Generalized Curvature Scale Space

Here, we intend to propose a 2D contour descriptor that we call Generalized Curvature Scale Space (GCSS) based on the iso-curvature levels, and the curvature scale space (CSS) descriptor. We start by computing the curvature in different scales and extract the points which have the same curvature values as the maximums in each scale. Each CSS image is represented by a set of key points. The Dynamic Time Warping (DTW) similarity measure is used. We reach a significant rate in image recognition using two data sets (HMM GPD and MPEG7 CE Shape-1 Part-B set).

Ameni Benkhlifa, Faouzi Ghorbel

A Coronary Artery Segmentation Method Based on Graph Cuts and MultiScale Analysis

In this paper we propose a new multi-scale fully automatic algorithm based on Graph cuts for vessel extraction. In fact, we combine vesselness, geodesic paths, a multi-scale edgeness map and the directional information for vessel tracking in order to personalize the Graph cuts approach to the segmentation of tubular structures.

Chaima Oueslati, Sabra Mabrouk, Faouzi Ghorbel, Mohamed Hedi Bedoui

Gaussian Bayes Classifier for 2D Shapes in Kendall Space

We propose a 2D-shape Gaussian Bayes classifier based upon Kendall’s representations that help to quotient out the effects of non-altering shape geometric transformations. The Kendall space is a non linear space that coincides with the unit sphere modulo an isometry group. The proposed Riemannian metric is more apt in the case where shapes are different only in translation, scale and rotation. In addition to that, the manifold structure of this space renders the multivariate statistical analysis implementation unfeasible in practice. Consequently, tools such as learning and classification models are non trivial and not frequently available. To overcome these issues, we adapt the Gaussian Bayes classifier to this space. We computed the likelihood parameters through appropriate projections onto Kendall tangent space that provides a good linear approximation. In order to validate the robustness of our classifier, we proceeded to computer simulations using several benchmarks.

Hibat Allah Rouahi, Riadh Mtibaa, Ezzeddine Zagrouba

Multimodal Image Fusion Based on Non-subsampled Shearlet Transform and Neuro-Fuzzy

Due to the appealing advantages in term of medical decision making, the problem of multimodal medical image fusion has received focused research over the recent years. Moreover, complimentary imaging modalities such as CT and MRI are able to improve medical reliability by reducing uncertainty. In this paper, we propose a new algorithm for multimodal medical image fusion based on non-subsampled shearlet transform (NSST) and neuro-fuzzy. Firstly, CT and MR source images are decomposed using the NSST to obtain low and high frequency sub-bands. Maximization of absolute value is performed to fuse low frequency coefficients while high frequency coefficients are fused using the neuro-fuzzy approach. Finally, the inverse NSST is performed to gain the fused image. To assess the performance of the proposed method, several experiments are carried on different medical CT and MR image datasets. Subjective and objective assessments reveal that the proposed scheme produces better results in various quantitative criterions compared to other existing methods.

Haithem Hermessi, Olfa Mourali, Ezzeddine Zagrouba


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