nils-herrmann/clean_scirepeval huggingface dataset:
UAVs for humanitarian missions: Autonomy and reliability

Information plays a key role in natural disaster crisis management and relief. A new generation of lightweight UAVs may help improve the situational awareness and assessment. They may first of all relieve rescue teams from time-consuming data collection tasks. At the same time, those UAVs may assist research operations through a more insightful and automated guidance thanks to advanced sensing capabilities. In order to achieve this vision, two challenges must be addressed though. The first one is to achieve a sufficient autonomy for such vehicles, both in terms of navigation and of interpretation of the data sensed. The second one relates to the reliability of the UAV with respect to accidental (safety) or malicious (security) risks. This paper first discusses the potential of UAV assistance in several humanitarian scenarios, as well as potential problems in such situations. The question of autonomy is then addressed. Finally, a secure embedded UAV architecture that relies on cryptographic protocols and on specific hardware capabilities is sketched.
New intra-video collusion attack using mosaicing

Recent efforts for watermarking digital video extend the results obtained for still image watermarking. As a result, most of the proposed algorithms rely on a frame-by-frame approach. Such an adaptation leads to unreliable algorithms in terms of security. The goal of this article is to stress the problem of collusion when digital watermarked data is distributed at large scale and especially intra-video collusion in the context of video. Three simple collusion attacks are described before being evaluated on two alternative video watermarking algorithms based on the spread spectrum technique. Finally, some experimental results are presented confirming the danger of intra-video collusion and some perspectives are discussed.
Protection of 3D Object Through Silhouette Watermarking

This paper describes a new approach for watermarking 3D objects via contour information. Unlike most conventional 3D object watermarking techniques, for which both insertion and extraction of the mark are performed on the object itself (3D/3D approach), we propose an asymmetric 3D/2D procedure. This procedure consists of watermarking 3D objects but retrieving the mark from represented views. In this paper we also propose an extension of 2D contour watermarking algorithm to 3D silhouette
Image sequence coding using 3-D IFS

We study the efficiency of the image sequence coding scheme based on 3-D iterated function systems (IFS). This coding scheme is an extension of the still image coding scheme proposed by Jacquin (1992). After a brief presentation of the IFS based coding and in particular the 3-D IFS scheme for coding image sequences, we simulate and discuss the already published schemes. We propose some improvements based on the choice of isometries and the construction of domain cubes during the coding.
Privacy protecting, intelligibility preserving video surveillance

Video surveillance is increasingly omni-present in our everyday life and is a key component of many security systems. Not only is the increasing number of cameras, but also the resolution of visual sensors and the performance of video processing algorithms. This evolution generates some important privacy concerns. This article introduces a new visual filter that includes a good trade-off between privacy and intelligibility. It ensures that people are unrecognizable while keeping the scene understandable in terms of events which allows machines to detect abnormal behavior. The algorithm operates in the DCT domain to be compliant with the popular JPEG and MPEG codecs. For each sensitive area of the picture (i.e. area where privacy needs to be protected), the proposed algorithm uses the low-frequency coefficients of the DCT to display a privacy preserved image of the region and the high-frequency coefficients to hide most of the original information. Finally, our process allows authorized users to nearly reverse the process thanks to the hidden information.
Exploring New Authentication Protocols for Sensitive Data Protection on Smartphones

Smartphones are increasingly becoming a tool for ubiquitous access to a number of services including, but not limited to, e-commerce and home banking, and are used more and more for sensitive data storage. If, on one hand, this makes the smartphone a powerful tool in our private and professional life, on the other hand it has brought about a series of new challenging security and privacy threats, and raised the need to protect users and their data through new secure authentication protocols. In this article, we illustrate how the security level of a human authentication system is increasing from traditional systems based on the use of passwords or badges to modern systems based on biometrics. We have moved a step forward by conceiving an authentication protocol based on the combined recognition of the human face and smartphone fingerprint. Thanks to image processing techniques, both the distinctive characteristics of the face and of the device that captures the face image can be extracted from a single photo or video frame and used for a double check of user identity. The fast technological development of smartphones allows performing sophisticated operations on the device itself. From the edge computing perspective, the burden of biometric recognition and source camera identification can be moved to the end user side.
Classification of facial expressions by sparse coding

Expression variations in facial images is one of the most crucial and difficult problems in face-based computer vision applications. Although numerous systems have been proposed for robustness against facial expressions, so far it still persists to be an open problem.Considering that the knowledge on the type of the expression in a facial image would greatly facilitate the solution of this issue, in this paper we present an analysis for facial expressions classification in 2D frontal views. With the motivation of the success that sparse coding achieved in face recognition, similar principals are applied for to both original and dimension-reduced (via PCA) images and the resulting codes are classified based on two different approaches: minimum residual error and maximum interclass summation of the coefficients. Extensive tests are conducted on Bosphorus database, in which different expressions are available for 105 persons.
Inpainting of sparse occlusion in face recognition

Facial occlusion is a critical issue in many face recognition applications. Existing approaches of face recognition under occlusion conditions mainly focus on the conventional facial accessories (such as sunglasses and scarf) and thus presume that the occluded region is dense and contiguous. Yet due to the wide variety of natural sources which can occlude a human face in uncontrolled environments, methods based on the dense assumption are not robust to thin and randomly distributed occlusions. This paper presents the solution to a newly identified facial occlusion problem - sparse occlusion in the context of face biometrics in video surveillance. We show that the occluded pixels can be detected in the low-rank structure of a canonical face set under the Robust-PCA framework; and the occluded part can be inpainted solely based on the nonoccluded part and a Fields-of-Experts prior via spatial inference. Experiments demonstrate that the proposed approach significantly improve various face recognition algorithms in presence of complex sparse occlusions.
3D Assisted Face Recognition: Dealing With Expression Variations

One of the most critical sources of variation in face recognition is facial expressions, especially in the frequent case where only a single sample per person is available for enrollment. Methods that improve the accuracy in the presence of such variations are still required for a reliable authentication system. In this paper, we address this problem with an analysis-by-synthesis-based scheme, in which a number of synthetic face images with different expressions are produced. For this purpose, an animatable 3D model is generated for each user based on 17 automatically located landmark points. The contribution of these additional images in terms of the recognition performance is evaluated with three different techniques (principal component analysis, linear discriminant analysis, and local binary patterns) on face recognition grand challenge and Bosphorus 3D face databases. Significant improvements are achieved in face recognition accuracies, for each database and algorithm.
Image-based gender estimation from body and face across distances

Gender estimation has received increased attention due to its use in a number of pertinent security and commercial applications. Automated gender estimation algorithms are mainly based on extracting representative features from face images. In this work we study gender estimation based on information deduced jointly from face and body, extracted from single-shot images. The approach addresses challenging settings such as low-resolution-images, as well as settings when faces are occluded. Specifically the face-based features include local binary patterns (LBP) and scale-invariant feature transform (SIFT) features, projected into a PCA space. The features of the novel body-based algorithm proposed in this work include continuous shape information extracted from body silhouettes and texture information retained by HOG descriptors. Support Vector Machines (SVMs) are used for classification for body and face features. We conduct experiments on images extracted from video-sequences of the Multi-Biometric Tunnel database, emphasizing on three distance-settings: close, medium and far, ranging from full body exposure (far setting) to head and shoulders exposure (close setting). The experiments suggest that while face-based gender estimation performs best in the close-distance-setting, body-based gender estimation performs best when a large part of the body is visible. Finally we present two score-level-fusion schemes of face and body-based features, outperforming the two individual modalities in most cases.
Unsupervised multi-view dimensionality reduction with application to audio-visual speaker retrieval

This paper presents a novel approach to unsupervised multi-view dimensionality reduction and reports its application to multi-modal biometrics retrieval, specifically audio-visual speaker retrieval. We propose a new concept referred to as multi-view subspace agreement, which aims to learn a subspace for each view which respects the similarity relationships between data points in the other view. The proposed algorithm is unsupervised but exhibits discriminative characteristics and is thus well suited to applications such as retrieval and clustering where class labels are generally unavailable. The effectiveness of the proposed algorithm is evaluated under an audio-visual speaker retrieval experiment with the MOBIO database. The retrieval performance of the proposed approach out-performs other single-view or multi-view dimensionality reduction methods with a significant margin.
Frontal-to-side face re-identification based on hair, skin and clothes patches

Despite recent advances, face-recognition algorithms are still challenged when applied in the setting of video surveillance systems which inherently introduce variations in the pose of subjects. The present work addresses this problem, and seeks to provide a recognition algorithm that is specifically suited for a frontal-to-side re-identification setting. Deviating from classical biometric approaches, the proposed method considers color- and texture- based soft biometric traits, specifically those taken from patches of hair, skin and clothes. The proposed method and the suitability of these patch-based traits are then validated both analytically and empirically.
Reversible Image Data Hiding with Contrast Enhancement

In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of trying to keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visual quality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can be performed by repeating the process. The side information is embedded along with the message bits into the host image so that the original image is completely recoverable. The proposed algorithm was implemented on two sets of images to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrast enhancement by RDH. Furthermore, the evaluation results show that the visual quality can be preserved after a considerable amount of message bits have been embedded into the contrast-enhanced images, even better than three specific MATLAB functions used for image contrast enhancement.
3D face recognition: A robust multi-matcher approach to data degradations

Over the past decades, 3D face has emerged as a solution to face recognition due to its reputed invariance to lighting conditions and pose. While proposed approaches have proven their efficiency over renowned databases as FRGC, less effort was spent on studying the robustness of algorithms to quality degradations. In this paper, we present a study of the robustness of four state of the art algorithms and a multi-matcher framework to face model degradations such as Gaussian noise, decimation, and holes. The four state of the art algorithms were chosen for their different and complementary properties and exemplify the major classes of 3D face recognition solutions. As they displayed different behavior under data degradations, we further designed a fusion framework to best take into account their complementary properties. The proposed multi-matcher scheme is based on an offline and an online weight learning process. Experiments were conducted on a subset of the FRGC database, on which we generated degradations. Results demonstrate the competitive robustness of the proposed approach.
A new multiclass SVM algorithm and its application to crowd density analysis using LBP features

Crowd density analysis is a crucial component in visual surveillance for security monitoring. In this paper, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions. The main contribution of this paper is two-fold: First, we propose to learn a discriminant subspace of the high-dimensional Local Binary Pattern (LBP) instead of using raw LBP feature vector. Second, an alternative algorithm for multiclass SVM based on relevance scores is proposed. The effectiveness of the proposed approach is evaluated on PETS dataset, and the results demonstrate the effect of low-dimensional compact representation of LBP on the classification accuracy. Also, the performance of the proposed multiclass SVM algorithm is compared to other frequently used algorithms for multi-classification problem and the proposed algorithm gives good results while reducing the complexity of the classification.
Improved combination of LBP and sparse representation based classification (SRC) for face recognition

Recently, local binary patterns (LBP) based descriptors and sparse representation based classification (SRC) become both eminent techniques in face recognition. Preliminary techniques of combining LBP and SRC have been proposed in the literature. However, the state-of-art method suffers from the “curse of dimensionality” for real world scenarios. In this paper, a novel face recognition algorithm of combining LBP with SRC is proposed; in which the dimensionality problem is resolved by divide-and-conquer and the discriminative power is strengthen via its pyramid architecture. The proposed face recognition method is evaluated on AR Face Database and yields very impressive results.
Applications and specificities of synthetic/synthetic projective registration

A recent application related to 3D watermarking has led to a specific registration problem: the registration of a 3D computer object with a computer-generated 2D view of it. So far, projective registration algorithms have focused on images of real objects because there was no interest in registering synthetic images with computer models. While those algorithms could also be directly applied to the case of synthetic images, they do not take advantage of some specificities of the synthetic/synthetic registration problem. This problem is addressed here and a dedicated registration algorithm is presented.
Danger of low-dimensional watermarking subspaces

The security issue has been neglected for a long time in digital watermarking. Recent results for video watermarking have pointed out that existing watermarking schemes are not secure, i.e., a hostile intelligence succeeds in removing the hidden watermarks. In particular, for a given secret key, many watermarking schemes embed watermarks which lie in the same low-dimensional subspace whatever the host data is. We show that this subspace can be quite easily estimated with an efficient principal component analysis (PCA). For storage convenience, an online expectation-maximization (EM) algorithm is considered. Once this watermarking subspace has been estimated, an attacker only has to project incoming data onto the orthogonal of this subspace to remove the watermark.
Regional confidence score assessment for 3D face

3D shape data for face recognition is advantageous to its 2D counterpart for being invariant to illumination and pose. However, expression variations and occlusions still remain as major challenges since the shape distortions hinder accurate matching. Numerous algorithms developed to overcome this problem mainly propose region-based approaches, where similarity scores are calculated separately by local regional matchers and fused for recognition. In this paper, we present a regional confidence score assessment scheme that estimates the expression or occlusion induced distortions in different facial regions. Thereby, reliability scores are obtained which can be used in fusion step for recognition. For 7 regions of face, primitive shape distributions are extracted and the surface quality is measured automatically by an Iterative Closest Point (ICP) based method. Using these measurements, an Artificial Neural Network (ANN) is trained and utilized to estimate regional reliability scores. Experiments have been conducted on FRGC v2 3D face database and results demonstrate a high accuracy in surface quality estimation.
Efficient ocular expression analysis for synthetic reproduction

This paper presents an original framework that analyzes the complete ocular (eye + eyebrow) expression on video sequences to reproduce it later on synthetic three-dimensional (3-D) models in real-time. We propose a two step process to develop robust techniques for facial feature analysis aimed at working without special illumination conditions or physical constraints on the user (markers, fixed frontal pose, etc.). First, simple and efficient image-processing methods based on motion models are designed over a frontal point of view of the face. Natural and realistic intra-feature and inter-feature constraints are applied to improve the analysis results. Then, the usability of these algorithms is extended to enable the analysis regardless of the person's pose in front of the camera. This is achieved by redefining the motion models involved over the speaker's highly realistic synthetic representation (clone), by using a suitable observation model, and by predicting the head pose in 3-D, frame by frame.
Sparse Feature Tracking for Crowd Change Detection and Event Recognition

The study of crowd behavior in public areas or during some public events is receiving a lot of attention in security community to detect potential risk and to prevent overcrowd. In this paper, we propose a novel approach for change detection and event recognition in human crowds. It consists of modeling time-varying dynamics of the crowd using local features. It also involves a feature tracking step which allows excluding feature points on the background and extracting long-term trajectories. This process is favourable for the later crowd event detection and recognition since the influence of features irrelevant to the underlying crowd is removed and the tracked features undergo an implicit temporal filtering. These feature tracks are further employed to extract regular motion patterns such as speed and flow direction. In addition, they are also used as an observation of a probabilistic crowd function to generate fully automatic crowd density maps. Finally, the variation of these attributes (local density, speed, and flow direction) in time is employed to determine the ongoing crowd behavior. The experimental results on two different crowd datasets demonstrate the effectiveness of our proposed approach for early detection of crowd change and accurate results for event recognition.
Block based face recognition approach robust to nose alterations

Face recognition that is robust to alterations applied on face via plastic surgery or prosthetic make-up can be still considered as a new topic. In this paper, a block based face analysis approach is proposed which provides a fairly good recognition performance together with the advantage of robustness to such kind of alterations. For this study, a simulated nose alteration face database is used which is prepared using FRGC v1.0. Since this is a 3D database, the approach can be tested both in 2D and 3D, which is one of the contributions of this study. Furthermore, differently from previous works, baseline results for the original database are reported. The impact which is purely due to the applied nose alterations is measured using both the proposed approach and the standard techniques which are based on holistic description for comparison. The results indicate that although both 2D and 3D modalities lose precision due to alterations, the proposed approach is superior in terms of both the recognition performance and robustness to alterations compared to standard techniques.
Color based soft biometry for hooligans detection

Biometric systems based on human traits like fingerprinting, face, iris, etc., even widely explored in literaure, cannot be used in scenarios like video surveillance of crowded envinroments. A different class of biometry, usually referred as soft biometry, including individual's height, weight, skin color, clothes color, trajectory, etc., can be more easily extracted from cameras in a large network to provide some useful information about the users. In this work we focus our attention on the color of human clothes. We apply color based soft biometric to a specific scenario, i.e. to prevent hooligans fights, based on the color of the clothes they wear. A video surveillance system able to quickly identify the presence of rival teams fans in a specific location would be helpful in avoiding vandalism and destructive behaviour.
Fractal transform based large digital watermark embedding and robust full blind extraction

The aim of this demonstration is to present the ongoing performance of our R. and D. watermarking scheme software. The proposed illustrations cover a large panel of original images (in grey levels and colors), signatures and attacks. Evaluation is performed according to ratio, visibility and robustness. All the results (excepting Stirmark cracker) are obtained using a full blind extraction, in other words the extraction step requires neither the original image nor the signature itself.