nils-herrmann/clean_scirepeval huggingface dataset:
Robust 3D face recognition in uncontrolled environments

Most current 3D face recognition algorithms are designed based on the data collected in controlled situations, which leads to the un-guaranteed performance in practical systems. In this paper, we propose a Robust Local Log-Gabor Histograms (RLLGH) method to handle the uncontrolled problems encountered in 3D face recognition. In this challenging topic, large expressions and data noises are two main obstacles. To overcome the large expressions, we choose Log-Gabor features (LGF) to extract the distinctive and robust information embedded in 3D faces, which will be represented as 3D Log-Gabor faces. Data noises are summarized as distorted meshes, hair occlusions and misalignments. To overcome these problems, we introduce a robust local histogram (RLH) strategy, which takes advantage of the robustness of the accurate local statistical information. The combination of LGF and RLH leads to RLLGH. The novelties of this paper come from 1) Our work aims at studying 3D face recognition performance in uncontrolled environments; 2) We find that embedding LGF into the LVC framework leads to robustness in handling large expression variations; 3) The RLH strategy gives a promising way to solve the data noises problem. Our experiments are based on the large expression subset in FRGC2.0 3D face database and the expression subset in CASIA 3D face database. Experimental results show the efficiency, robustness and generalization of our proposed method.
Supervised topology preserving hashing

Learning based hashing is gaining traction in large-scale retrieval systems. It aims to learn compact binary codes that can preserve semantic similarity in the hamming space. This paper presents a supervised topology hashing (SPTH) algorithm to learn compact binary codes that can exploit both the supervisory information as well as the local topology structure of datasets. To build a connection between the original space and the resultant hamming space, we minimize the quantization errors together with a classification error term and a topology preserving term. A nonlinear kernel feature space is further used to improve the generalization power. An alternating iterative algorithm is developed to minimize the complex objective function that contains both continuous and discrete variables. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method on image retrieval tasks.
Online codebook reweighting using pairwise constraints for image classification

Bag-of-words (BoW) model is widely used for image classification. Recently, the framework of sparse coding and max pooling proved an effective approach for image classification. Max pooling adopts a winner-take-all strategy. Thus, it can be regarded as a codebook weighting process. The results of this process are the weights of the associated codebook. However, there are high intra-class variations and strong background clutters in many image classification tasks. The weights obtained by max pooling only have limited information. This paper presents a codebook reweighting algorithm using pairwise constraints to improve the performance of sparse coding and max pooling framework. Pairwise constraints are the natural way of encoding the relationships between pairs of images. Therefore, the reweighted codebook is more effective to describe the relevance between pairs of images. An efficient online learning algorithm is presented based on passive-aggressive training strategy. We compare our method with other state-of-the-art methods on Graz-01 & 02 datasets. Experimental results illustrate the effectiveness and efficiency of our method for image classification.
Exploring DCT Coefficient Quantization Effects for Local Tampering Detection

In this paper, we focus on local image tampering detection. For a JPEG image, the probability distributions of its DCT coefficients will be disturbed by tampering operation. The tampered region and the unchanged region have different distributions, which is an important clue for locating tampering. Based on the assumption of Laplacian distribution of unquantized ac DCT coefficients, these two distributions as well as the size of tampered region can be estimated so that the probability of each DCT block being tampered is obtained. More accurate localization results could be got when we consider the prior knowledge of common tampered regions. We also design three kinds of features that can distinguish truly tampered regions from the false ones to reduce false alarm. For a tampered image which is saved in lossless compressed format, we also propose the specialized approach, which employs the quantization noise of high-frequency DCT coefficient, to improve the tampering localization performance. Extensive experiments on large scale databases prove the effectiveness of our proposed method and demonstrate that our method is suitable for locating tampered regions with different scales.
Gait recognition based on Procrustes shape analysis

Gait recognition has recently attracted increasing attention, especially in vision-based human identification-at-a-distance in visual surveillance. The paper proposes a simple but efficient gait recognition algorithm, based on statistical shape analysis. For each gait sequence, a background subtraction procedure is used to segment spatial silhouettes of the walking figures from the background. Static pose changes of these silhouettes over time are represented as a sequence of associated complex configurations in a common coordinate, and are then analyzed using the Procrustes shape analysis method to obtain a gait signature. The k-nearest neighbor classifier and the nearest exemplar classifier based on the full Procrustes distance measure are adopted for recognition. Experimental results demonstrate that the proposed algorithm has an encouraging recognition performance.
Content-based watermarking model

We propose a general additive watermarking model based on the content of digital images, called CBWM (content-based watermarking model). It provides a common basis to study many existing watermarking algorithms that are based on unitary transforms and to evaluate their performance and characteristics. CBWM is designed to address two important issues of digital watermarking. One is the requirement of robustness. In order to improve robustness and security, the embedded watermark is designed to be orthogonal to the feature vector of the original image, which means that watermarking casting is image content dependent. The second issue is watermark detection. CBWM presents a statistical approach to watermark detection based on the Neyman-Pearson criterion and describes a method for computing the probability of false alarm and missing rejection during watermark detection. In experimental tests, CBWM is applied to the popular discrete cosine transform and promising results are obtained.
Robust automated ground plane rectification based on moving vehicles for traffic scene surveillance

Most outdoor visual surveillance scenes involve objects of interest moving on the ground plane. However, perspective distortion introduces many difficulties to various applications like object classification and activity recognition. In this paper, we propose a robust automated method for both affine and metric rectification of the ground plane based on appearance and motion of vehicles in traffic scene surveillance videos. This rectification enables normalization of object properties like size, length and velocity. Various useful applications are presented and experimental results demonstrate the effectiveness and robustness of the proposed method.
Robust Subspace Clustering via Half-Quadratic Minimization

Subspace clustering has important and wide applications in computer vision and pattern recognition. It is a challenging task to learn low-dimensional subspace structures due to the possible errors (e.g., noise and corruptions) existing in high-dimensional data. Recent subspace clustering methods usually assume a sparse representation of corrupted errors and correct the errors iteratively. However large corruptions in real-world applications can not be well addressed by these methods. A novel optimization model for robust subspace clustering is proposed in this paper. The objective function of our model mainly includes two parts. The first part aims to achieve a sparse representation of each high-dimensional data point with other data points. The second part aims to maximize the correntropy between a given data point and its low-dimensional representation with other points. Correntropy is a robust measure so that the influence of large corruptions on subspace clustering can be greatly suppressed. An extension of our method with explicit introduction of representation error terms into the model is also proposed. Half-quadratic minimization is provided as an efficient solution to the proposed robust subspace clustering formulations. Experimental results on Hopkins 155 dataset and Extended Yale Database B demonstrate that our method outperforms state-of-the-art subspace clustering methods.
Deformed iris recognition using bandpass geometric features and lowpass ordinal features

Deformation of iris pattern caused by pupil dilation and contraction is one of the most influential intra-class variations. Most state-of-the-art iris recognition methods only focus on the description of local iris texture features. We believe that both geometric and photometric features are important to achieve a robust matching result of deformed iris images. This paper proposes to decompose iris images into lowpass and bandpass components using nonsubsampled contourlet transform (NSCT) and then extract different features. Geometric features are extracted in bandpass components based on key point detection to align deformed iris patterns. And then aligned Ordinal features are extracted in lowpass components to characterize the ordinal measures of local iris regions. Finally, key point features in bandpass components and Ordinal features in lowpass components are fused for deformed iris image matching. Extensive experiments on two challenging iris image databases namely CASIA-Iris-Lamp and ICE'2005 demonstrate that the proposed method outperforms state-of-the-art methods in deformed iris recognition.
Iris Matching Based on Personalized Weight Map

Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.
Learning and transferring representations for image steganalysis using convolutional neural network

The major challenge of machine learning based image steganalysis lies in obtaining powerful feature representations. Recently, Qian et al. have shown that Convolutional Neural Network (CNN) is effective for learning features automatically for steganalysis. In this paper, we follow up this new paradigm in steganalysis, and propose a framework based on transfer learning to help the training of CNN for steganalysis, hence to achieve a better performance. We show that feature representations learned with a pre-trained CNN for detecting a steganographic algorithm with a high payload can be efficiently transferred to improve the learning of features for detecting the same steganographic algorithm with a low pay-load. By detecting representative WOW and S-UNIWARD steganographic algorithms, we demonstrate that the proposed scheme is effective in improving the feature learning in CNN models for steganalysis.
Synthesis of large realistic iris databases using patch-based sampling

This paper presents a framework to synthesize large realistic iris databases, providing an alternative to iris database collection. Firstly, iris patch is used as a basic element to characterize visual primitive of iris texture, and patch-based sampling is applied to create an iris prototype. Then a set of pseudo irises with intra-class variations are derived from the prototype. Qualitative and quantitative studies reveal that synthetic databases are well suited for evaluating iris recognition systems by achieving three goals: (1) the synthetic iris images bear a close resemblance to real iris images in terms of visual appearance; (2) the proposed framework is able to generate databases with large capacity; (3) statistical performance shows that the synthetic iris images hold all the major characteristics of real iris images.
Mobile robot self-localization based on global visual appearance features

The paper presents a novel method for mobile robot localization using visual appearance features. A multidimensional histogram is used to describe the global appearance features of an image such as colors, edge density, gradient magnitude, textures and so on. The matching of histograms determines the location of the robot. The method has been evaluated in an indoor environment, and the system correctly determines the location of 82.9% of the input scene images.
Foreground Object Detection Using Top-Down Information Based on EM Framework

In this paper, we present a novel foreground object detection scheme that integrates the top-down information based on the expectation maximization (EM) framework. In this generalized EM framework, the top-down information is incorporated in an object model. Based on the object model and the state of each target, a foreground model is constructed. This foreground model can augment the foreground detection for the camouflage problem. Thus, an object's state-specific Markov random field (MRF) model is constructed for detection based on the foreground model and the background model. This MRF model depends on the latent variables that describe each object's state. The maximization of the MRF model is the M-step in the EM framework. Besides fusing spatial information, this MRF model can also adjust the contribution of the top-down information for detection. To obtain detection result using this MRF model, sampling importance resampling is used to sample the latent variable and the EM framework refines the detection iteratively. Besides the proposed generalized EM framework, our method does not need any prior information of the moving object, because we use the detection result of moving object to incorporate the domain knowledge of the object shapes into the construction of top-down information. Moreover, in our method, a kernel density estimation (KDE)—Gaussian mixture model (GMM) hybrid model is proposed to construct the probability density function of background and moving object model. For the background model, it has some advantages over GMM- and KDE-based methods. Experimental results demonstrate the capability of our method, particularly in handling the camouflage problem.
A feature-level solution to off-angle iris recognition

For iris recognition, it is inevitable to encounter a large portion of off-angle iris images in less constrained conditions. This paper proposes a feature-level solution to off-angle iris recognition which is less dependent on iris image preprocessing. Firstly, we use geometric features of corneal reflections and multiclass SVM to classify iris images into five categories (i.e., frontal, right, left, up and down) according to the off-angle orientation of iris region. And then a feature learning method based on linear programming is used to select the most effective ordinal features of each iris category. Finally, the input off-angle iris image is recognized with the specific ordinal feature template belonging to the corresponding iris category. Experimental results on the Clarkson Angle database demonstrate that our feature-level solution significantly outperforms the mainstream methods based on off-angle iris image preprocessing.
A Study on Gait-Based Gender Classification

Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
Joint space learning for video-based face recognition

Popularity of surveillance and mobile cameras provides great opportunities to video-based face recognition (VFR) in less-controlled conditions. This paper proposes a joint space learning method to simultaneously identify the most representative samples and discriminative features from facial videos for reliable face recognition. Specifically, we use a mixture modal by learning multiple feature spaces to capture the data variations where the representative samples in each subspace are learned. Actually, this procedure is a chick to egg problem and an alternate algorithm is developed to monotonically optimize the joint task. In addition, randomized techniques are applied to kernel approximations for capturing the nonlinear structure in data, so that both accuracy and efficiency of our method can be improved. The proposed method performs better than the state-of-the-art video based face recognition methods on Honda, Mobo and YouTube Celebrities databases.
Principal affinity based cross-modal retrieval

Multimedia content is increasingly available in multiple modalities. Each modality provides a different representation of the same entity. This paper studies the problem of joint representation of the text and image components of multimedia documents. However, most existing algorithms focus more on inter-modal connection rather than intramodal feature extraction. In this paper, a simple yet effective principal affinity representation (PAR) approach is proposed to exploit the affinity representations of different modalities with local cluster samples. Afterwards, multi-class logistic regression model is adopted to learn the projections from principal affinity representation to semantic labels vectors. Inner product distance is further used to improve cross-modal retrieval performance. Extensive experiments on three benchmark datasets illustrate that our proposed method obtains significant improvements over the state-of-the-art subspace learning based cross-modal methods.
Graph modeling based local descriptor selection via a hierarchical structure for biometric recognition

Local descriptor based image representation is widely used in biometrics and has achieved promising results. We usually extract the most distinctive local descriptors for image sparse representation due to the large feature space and the redundancy among local descriptors. In this paper, we describe the local descriptor based image representation via a graph model, in which each node is a local descriptor (we call it “atom”) and the edges denote the relationship between atoms. Based on this model, a hierarchical structure is constructed to select the most distinctive local descriptors. Two-layer structure is adopted in our work, including local selection and global selection. In the first layer, L1/Lq regularized least square regression is adopted to reduce the redundancy of local descriptors in local regions. In the second layer, AdaBoost learning is performed for local descriptor selection based on the results of the first layer. We apply this method to long-range personal identification by using binocular regions. Our method can select the distinctive local descriptors and reduce the redundancy among them, and achieve encouraging results on the collected binocular database and CASIA-Iris-Distance. Particularly, our method is about 50 times faster than the traditional AdaBoost learning based method in the experiments.
Traffic accident prediction using vehicle tracking and trajectory analysis

Intelligent visual surveillance for road vehicles is a key component for developing autonomous intelligent transportation systems. In this paper, a probabilistic model for prediction of traffic accidents using 3D model based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3D model based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Vehicle activities are finally predicted by locating and matching each observed partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments with a model scene show the effectiveness of the proposed algorithm.
Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback

Pairwise learning algorithms are a vital technique for personalized ranking with implicit feedback. They usually assume that each user is more interested in items which have been selected by the user than remaining ones. This pairwise assumption usually derives massive training pairs. To deal with such large-scale training data, the learning algorithms are usually based on stochastic gradient descent with uniformly drawn pairs. However, the uniformly sampling strategy often results in slow convergence. In this paper, we first uncover the reasons of slow convergence. Then, we associate contents of entities with characteristics of data sets to develop an adaptive item sampler for drawing informative training data. In this end, to devise a robust personalized ranking method, we accordingly embed our sampler into Bayesian Personalized Ranking (BPR) framework, and further propose a Content-aware and Adaptive Bayesian Personalized Ranking (CA-BPR) method, which can model both contents and implicit feedbacks in a unified learning process. The experimental results show that, our adaptive item sampler indeed can speed up BPR learning and CA-BPR definitively outperforms the state-of-the-art methods in personalized ranking.