nils-herrmann/clean_scirepeval huggingface dataset:
KinectFaceDB: A Kinect Database for Face Recognition

The recent success of emerging RGB-D cameras such as the Kinect sensor depicts a broad prospect of 3-D data-based computer applications. However, due to the lack of a standard testing database, it is difficult to evaluate how the face recognition technology can benefit from this up-to-date imaging sensor. In order to establish the connection between the Kinect and face recognition research, in this paper, we present the first publicly available face database (i.e., KinectFaceDB1) based on the Kinect sensor. The database consists of different data modalities (well-aligned and processed 2-D, 2.5-D, 3-D, and video-based face data) and multiple facial variations. We conducted benchmark evaluations on the proposed database using standard face recognition methods, and demonstrated the gain in performance when integrating the depth data with the RGB data via score-level fusion. We also compared the 3-D images of Kinect (from the KinectFaceDB) with the traditional high-quality 3-D scans (from the FRGC database) in the context of face biometrics, which reveals the imperative needs of the proposed database for face recognition research.
Impact analysis of nose alterations on 2D and 3D face recognition

Numerous major challenges in face recognition, such as pose, illumination, expression and aging, have been investigated extensively. All those variations modify the texture and/or the shape of the face in a similar manner for different individuals. However, studies on alterations applied on face via plastic surgery or prosthetic make-up which can be in countless different ways and amounts, are still very limited. In this paper, we analyze how such changes on nose region affect the face recognition performances of several key techniques. For this purpose, a simulated face database is prepared using FRGC v1.0 in which nose in each sample is replaced with another randomly chosen one. Since this is a 3D database, the impact analysis is not limited to only 2D, which is one of the novelties of this study. Performance comparisons of three 2D and four 3D algorithms are provided. In addition, differently from previous works, baseline results for the original database are also reported. Hence, the impact which is purely due to the applied nose alterations can be measured. The experimental results indicate that with the introduction of alterations both modalities lose precision, especially 3D.
Improving the recognition of faces occluded by facial accessories

Facial occlusions, due for example to sunglasses, hats, scarf, beards etc., can significantly affect the performance of any face recognition system. Unfortunately, the presence of facial occlusions is quite common in real-world applications especially when the individuals are not cooperative with the system such as in video surveillance scenarios. While there has been an enormous amount of research on face recognition under pose/illumination changes and image degradations, problems caused by occlusions are mostly overlooked. The focus of this paper is thus on facial occlusions, and particularly on how to improve the recognition of faces occluded by sunglasses and scarf. We propose an efficient approach which consists of first detecting the presence of scarf/sunglasses and then processing the non-occluded facial regions only. The occlusion detection problem is approached using Gabor wavelets, PCA and support vector machines (SVM), while the recognition of the non-occluded facial part is performed using block-based local binary patterns. Experiments on AR face database showed that the proposed method yields significant performance improvements compared to existing works for recognizing partially occluded and also non-occluded faces. Furthermore, the performance of the proposed approach is also assessed under illumination and extreme facial expression changes, demonstrating interesting results.
Shape and Texture Based Countermeasure to Protect Face Recognition Systems against Mask Attacks

Photographs, videos or masks can be used to spoof face recognition systems. In this paper, a countermeasure is proposed to protect face recognition systems against 3D mask attacks. The reason for the lack of studies on countermeasures against mask attacks is mainly due to the unavailability of public databases dedicated to mask attack. In this study, a 2D+3D mask attacks database is used that is prepared for a research project in which the authors are all involved. The proposed countermeasure is based on the fusion of the information extracted from both the texture and the depth images in the mask database, and provides satisfactory results to protect recognition systems against mask attacks. Another contribution of this study is that the countermeasure is integrated to the selected baseline systems for 2D and 3D face recognition, which provides to analyze the performances of the systems with/without attacks and with/without the countermeasure.
Enhancing human detection using crowd density measures and an adaptive correction filter

In this paper we present a method of improving a human detector by means of crowd density information. Human detection is especially challenging in crowded scenes which makes it important to introduce additional knowledge into the detection process. We compute crowd density maps in order to estimate the spatial distribution of people in the scene and show how it is possible to enhance the detection results of a state-of-the-art human detector by this information. The proposed method applies a self-adaptive, dynamic parametrization and as an additional contribution uses scene-adaptive learning of the human aspect ratio in order to reduce false positive detections in crowded areas. We evaluate our method on videos from different datasets and demonstrate how our system achieves better results than the baseline algorithm.
A Countermeasur to Resist Block Replacement Attacks

Security issues have almost been ignored during the first decade of digital watermarking. As a result, many released watermarking algorithms are weak against hostile intelligence. For instance, block replacement attacks defeat watermarking systems which do not consider the self-similarities of the host signal during embedding. Such attacks replace each signal block with another one, or a combination of other ones, taken at a different location. In this paper, a novel strategy will be presented to generate a signal coherent watermark to achieve immunity against block replacement attacks. The basic idea consists in imposing a linear relationship between watermark samples embedded at different locations, with respect to their local neighborhoods which are characterized with Gabor features.
From turbo hidden Markov models to turbo state-space models [face recognition applications]

We recently introduced a novel approximation of the intractable two-dimensional hidden Markov model (2D HMM), the turbo-HMM (T-HMM), which consists of a set of interconnected horizontal and vertical 1D HMMs. In this paper, we consider the extension of this framework to the continuous state HMM, generally referred to as the state-space model (SSM). We provide efficient approximate answers to the three following problems: (1) how to compute the likelihood of a set of observations; (2) how to find the sequence of states that best "explains" a set of observations; and (3) how to estimate the model parameters given a set of observations. The application of this work to the challenging problem of face recognition, in the presence of large illumination variations, illustrates the potential of our approach.
The image Text Recognition Graph (iTRG)

This paper presents a graph based scheme for color text recognition in images and videos, which is particularly robust to complex background, low resolution or video coding artifacts. This scheme is based on a novel method named the image Text Recognition Graph (iTRG) composed of five main modules: an image text segmentation module, a graph connection builder module, a character recognition module, a graph weight calculator module and an optimal path search module. The first two modules are based on convolutional neural networks so that the proposed system automatically learns how to robustly perform segmentation and recognition. The proposed method is evaluated on the public ICDAR 2003 test word dataset.
Deformable face mapping for person identification

This paper introduces a novel deformable model for face mapping and its application to automatic person identification. While most face recognition techniques directly model the face, our goal is to model the transformation between face images of the same person. As a global face transformation may be too complex to be modeled in its entirety, it is approximated by a set of local transformations with the constraint that neighboring transformations must be consistent with each other. Local transformations and neighboring constraints are embedded within the probabilistic framework of a two-dimensional hidden Markov model (2-D HMM). Experimental results on a face identification task show that the new approach compares favorably to the popular Fisherfaces algorithm.
Semi-supervised face recognition with LDA self-training

Face recognition algorithms based on linear discriminant analysis (LDA) generally give satisfactory performance but tend to require a relatively high number of samples in order to learn reliable projections. In many practical applications of face recognition there is only a small number of labelled face images and in this case LDA-based algorithms generally lead to poor performance. The contributions in this paper relate to a new semi-supervised, self-training LDA-based algorithm which is used to augment a manually labelled training set with new data from an unlabelled, auxiliary set and hence to improve recognition performance. Without the cost of manual labelling such auxiliary data is often easily acquired but is not normally useful for learning. We report face recognition experiments on 3 independent databases which demonstrate a constant improvement of our baseline, supervised LDA system. The performance of our algorithm is also shown to significantly outperform other semi-supervised learning algorithms.
A fractals-inspired approach to content-based image indexing

This paper applies ideas from fractal compression and optimization theory to attack the problem of efficient content-based image indexing and retrieval. Similarity of images is measured by block matching after optimal (geometric, photometric, etc.) transformation. Such block matching which, by definition, consists of localized optimization, is further governed by a global dynamic programming technique (Viterbi algorithm) that ensures continuity and coherence of the localized block matching results. Thus, the overall optimal transformation relating two images is determined by a combination of local block-transformation operations subject to a regularization constraint. Experimental results on a sample of seventy five binary images from the MPEG-7 database demonstrate the power and potential of the proposed approach.
A Markov Random Field description of fuzzy color segmentation

Image segmentation is a fundamental task in many computer vision applications. In this paper, we describe a new unsupervised color image segmentation algorithm, which exploits the color characteristics of the image. The introduced system is based on a color quantization of the image in the Lab color space using the popular eleven culture colors in order to avoid the well known problem of oversegmentation. To partially overcome the problem of highlight and shadows in the image, which is one of the main aspect affecting the performance of color segmentation systems, the proposed approach uses a fuzzy classifier trained on an ad-hoc designed dataset. A Markov Random Field description of the full algorithm is moreover provided which helps to remove resilient errors trough the use of an iterative strategy. The experimantal results show the good performance of the proposed approach which is comparable to state of the art systems even if based only on the color information of the image.
Moving face spoofing detection via 3D projective invariants

Face recognition provides many advantages compared with other available biometrics, but it is particularly subject to spoofing. The most accurate methods in literature addressing this problem, rely on the estimation of the three-dimensionality of faces, which heavily increase the whole cost of the system. This paper proposes an effective and efficient solution to problem of face spoofing. Starting from a set of automatically located facial points, we exploit geometric invariants for detecting replay attacks. The presented results demonstrate the effectiveness and efficiency of the proposed indices.
Facial expression analysis robust to 3D head pose motion

Most face expression algorithms assume a front or 'near-to-front' head position. This assumption becomes an important limitation when studying input from real systems. We present a new approach to robustly determine face expressions independently of the head pose. Our analysis-synthesis cooperation, possible thanks to the use of a highly realistic 3D head model and the application of Kalman filtering to predict the user pose, permits us to correctly track the interesting face features. Adapting 'near-to-front' analysis techniques based on the predicted pose enables us to use such algorithms with moving speakers.
Building the space scale or how to weigh a person with no gravity

Since the very beginning of space exploration, cosmonauts have suffered from weight losses which need to be particularly monitored during long term missions in space stations to insure their health and well being. In 1965-6 Thornton successfully built a device able to measure the body mass of cosmonauts in the micro-gravity space environment using passive linear spring-mass oscillators. Since then, space stations have been equipped with labs containing, among others, bulky devices like Thornton's. In this work we report recent advancements in computer vision algorithms allowing us to estimate the weight of a person within 4% error using 2D and 3D data extracted from a low-cost Kinect RGBD camera output.
Autonomous drones for assisting rescue services within the context of natural disasters

Information plays a key role to correctly handle consequences resulting from natural disasters. Discharging rescue teams from gathering that information, and automatically guiding rescue teams to most urgent sub-situations is an open issue in which mini-drones can be useful. Yet, the control of such vehicles is not straight forward to users and can be time consuming. Thus, our contribution is to bring autonomy to drones: to fly autonomously, e.g., scanning and covering a given area, and to realize some tasks (e.g., identifying groups of disabled persons). Last but not least, autonomous drones shall be able to perform both outdoor and indoor missions.
On the reliability of eye color as a soft biometric trait

This work studies eye color as a soft biometric trait and provides a novel insight about the influence of pertinent factors in this context, like color spaces, illumination and presence of glasses. A motivation for the paper is the fact that the human iris color is an essential facial trait for Caucasians, which can be employed in iris pattern recognition systems for pruning the search or in soft biometrics systems for person re-identification. Towards studying iris color as a soft biometric trait, we consider a system for automatic detection of eye color, based on standard facial images. The system entails automatic iris localization, followed by classification based on Gaussian Mixture Models with Expectation Maximization. We finally provide related detection results on the UBIRIS2 database employable in a real time eye color detection system.
Real time 3D navigation in a static virtualized scene from a limited set of 2D data

Among the new data processing services and applications, virtual navigation in a real 3D world takes more and more importance and requires specific research. With regard to the reactions of the most important part of net surfers for example, we think that for a user of an interactive immersive tool, it is absolutely necessary to obtain a real time rendering of the virtual world, as natural and realistic as possible. That is why we focus our work on an image based approach, avoiding the slow and heavy handling of 3D models of the scene, generally not very realistic and typical in geometry based approaches. Our method uses for its part the synthesis of photorealistic views based on the trilinearity theory combined with image mosaicking. It especially minimizes the algebraic processing needed for view synthesis in order to offer in real time a natural rendering of the scene to the user, according to his/her relative movement from his/her initial position.
Image reversible visual transformation based on MSB replacement and histogram bin mapping

An image reversible transformation method is proposed for secret image transmission and privacy protection. With the proposed method, the visual content of a secret image is changed to mimic a target image while the secret image can be recovered from the transformed image. Different from the previous methods that divide the secret image into blocks, our method processes the secret image as a whole so that no block artifact will be caused. Firstly, the most significant bit (MSB) plane of the secret image is replaced and then a process called histogram bin mapping is conducted. The replaced MSB plane and histogram bin mapping information are further hidden into the transformed image. Via reversible data hiding, the secret image can be exactly recovered by extracting the hidden data and reversing the transformation process. The experimental results show that the visual content of secret images can be semantically changed, while almost all of the test images were successfully recovered from the corresponding transformed images.
Face dynamics for biometric people recognition

Biometric systems have gained the attention of both the research community and the industry becoming an important topic in real application scenarios. Face recognition is, with fingerprint, among the most used techniques since it is natural for humans to recognize people from facial appearance, since the technology is mature, and because, unlike fingerprint, it is completely unintrusive. Existing systems only focus on the appearance of the subjects considering facial expressions as an obstacle to their aim. On the other hand such systems presents several limitations when dealing with variable illumination conditions, head pose, day-to-day variations (e.g. beard, glasses, or make-up), etc. Furthermore, most of the current techniques do not exploit dynamics to detect the liveness of the tested subjects. In this paper we present a study on person recognition from the dynamics of the facial feature points. The aim of this work is to demonstrate that dynamics of facial expressions could be seen as a biometric characteristic. Therefore, only dynamic characteristics are considered and the adopted features are purged of all appearance information. The results clearly show that relevant biometric information can be extracted from facial expressions and other dynamics of the face.
Video Face Recognition: A Physiological and Behavioural Multimodal Approach

In this article we present a multimodal system to person recognition by integrating two complementary approaches that work with video data. The first module exploits the behavioural information: it is based on statistical features computed using the displacement signals of a head; the second one is dealing with the physiological information: it is a probabilistic extension of the classic Eigenface approach. For a consistent fusion, both systems share the same probabilistic classification framework: a Gaussian mixture model (GMM) approximation and a Bayesian classifier. We assess the performances of the multimodal system by implementing two fusion strategies and we analyse their evolution in presence of artificial noise.
Watermarking video, hierarchical embedding in motion vectors

This paper proposes a new video watermarking scheme based on a hierarchical motion analysis, that is robust against classical video processing (filtering, lossy compression...). This model works in the uncompressed domain and disturbs the motion vectors computed by an exhaustive BMA on blocks of size n*n in order to insert an invisible watermark enabling a protection against most attacks. Finally, to increase the robustness, our system inserts the mark by generating a hierarchy of motion vectors of size N*N with N=k*n to spread the mark on the lower level associated to block n*n.
Scrambling faces for privacy protection using background self-similarities

The pervasive adoption of video surveillance systems demands tools for protecting the privacy of the persons being monitored. Current solutions are either naïve or they lack of important characteristics, such as reversibility or visual quality preservation. In this paper, we propose a novel scrambling procedure for protecting privacy sensitive image regions, which encodes the sensitive data in a parametric form, exploiting the visual information in the remaining part of the image. The encoded data is encrypted with a secret key. Partial knowledge of encryption key gives a protected version of the original image at variable levels of scrambling, while the knowledge of the full key allows decryption to a quality level suitable for people identification. To evaluate the proposed approach, we apply our scrambling filter to the AT&T face recognition dataset and we measure the resulting quality with an objective metric.
De-genderization by body contours reshaping

This paper deals with privacy protection in video surveillance. More specifically, the main goal of this work is to make the gender of people no more recognizable while preserving enough information concerning body shape and motion of people for action classification. We denote this processing as de-genderization. Regarding the current state-of-art methods, most of them have privacy filters only dedicated to de-identify people. These methods do not automatically imply the suppression of visual semantic traits such as gender. Therefore, we propose two approaches that modify the visual appearance of the body shape in order to de-genderize people while keeping the possibility to interpret the video. In both methods we start by extracting the contour points attached to the body shape of people in videos. Then we either mix the coordinates of the body shape and a predefined model, or we smooth the body shape by successive polygonal approximations based on convexity. Our results demonstrate that both proposed approaches protect the gender information while preserving the global body movement. The second approach based on convexity better preserves the visibility of human activities.
A probabilistic model of face mapping with local transformations and its application to person recognition

This paper proposes a new measure of "distance" between faces. This measure involves the estimation of the set of possible transformations between face images of the same person. The global transformation, which is assumed to be too complex for direct modeling, is approximated by a patchwork of local transformations, under a constraint imposing consistency between neighboring local transformations. The proposed system of local transformations and neighboring constraints is embedded within the probabilistic framework of a two-dimensional hidden Markov model. More specifically, we model two types of intraclass variabilities involving variations in facial expressions and illumination, respectively. The performance of the resulting method is assessed on a large data set consisting of four face databases. In particular, it is shown to outperform a leading approach to face recognition, namely, the Bayesian intra/extrapersonal classifier.