nils-herrmann/clean_scirepeval huggingface dataset:
A Light CNN for Deep Face Representation With Noisy Labels

The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data. When training data are obtained from the Internet, the labels are likely to be ambiguous and inaccurate. This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels. First, we introduce a variation of maxout activation, called max-feature-map (MFM), into each convolutional layer of CNN. Different from maxout activation that uses many feature maps to linearly approximate an arbitrary convex activation function, MFM does so via a competitive relationship. MFM can not only separate noisy and informative signals but also play the role of feature selection between two feature maps. Second, three networks are carefully designed to obtain better performance, meanwhile, reducing the number of parameters and computational costs. Finally, a semantic bootstrapping method is proposed to make the prediction of the networks more consistent with noisy labels. Experimental results show that the proposed framework can utilize large-scale noisy data to learn a Light model that is efficient in computational costs and storage spaces. The learned single network with a 256-D representation achieves state-of-the-art results on various face benchmarks without fine-tuning.
Hierarchical Fusion of Face and Iris for Personal Identification

Most existing face and iris fusion schemes are concerned about improving performance on good quality images under controlled environments. In this paper, we propose a hierarchical fusion scheme for low quality images under uncontrolled situations. In the training stage, canonical correlation analysis (CCA) is adopted to construct a statistical mapping from face to iris in pixel level. In the testing stage, firstly the probe face image is used to obtain a subset of candidate gallery samples via regression between the probe face and gallery irises, then ordinal representation and sparse representation are performed on these candidate samples for iris recognition and face recognition respectively. Finally, score level fusion via min-max normalization is performed to make final decision. Experimental results on our low quality database show the outperforming performance of proposed method.
Efficient and robust vehicle localization

We propose a novel algorithm for efficient and robust pose determination of vehicles in traffic scenes from single monocular intensity images using calibrated cameras. We consider the pose determination process as a series of evolutions from initial pose to correct pose in 3D space, which can be decomposed into two independent 3D motions: translation and rotation. The translation parameters are obtained based on point-to-line-segment distance (PLS distance), while the rotation parameters are determined by geometric relationships among a set of specially constructed but imaginary planes. Closed-form solutions to both sub-problems are obtained, thus avoiding the usual shortcoming of relatively high computational cost of traditional 3D-model based approaches. In addition, vertex neighborhood constraint (VNC) is introduced to improve the robustness of the method. Experimental results show that the algorithm works well even under severe occlusion and clutter.
Improving iris recognition accuracy via cascaded classifiers

As a reliable approach to human identification, iris recognition has received increasing attention in recent years. The most distinguishing feature of an iris image comes from the fine spatial changes of the image structure. So iris pattern representation must characterize the local intensity variations in iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, iris localization error, nonlinear iris deformations, etc. This greatly limits the accuracy of iris recognition systems. In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various iris images efficiently a novel cascading scheme is proposed to combine the LFC and an iris blob matcher. When the LFC is uncertain of its decision, poor quality iris images are usually involved in intra-class comparison. Then the iris blob matcher is resorted to determine the input iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost.
Direction-based stochastic matching for pedestrian recognition in non-overlapping cameras

Pedestrian recognition is a challenging problem in non-overlapping multi-camera object tracking. In this paper, we present a novel approach for matching pedestrians across non-overlapping multiple cameras without the need of a training phase or spatio-temporal cues across cameras. To deal with viewpoint changes, we introduce the concept of directional angles estimated using the spatio-temporal continuity in the single camera tracking. To deal with pose changes, a stochastic matching strategy is performed, where the similarity of two blobs belonging to different viewpoints is calculated by a novel similarity measurement algorithm. The experiments are performed on different multi-view datasets. Experimental results demonstrate the effectiveness and robustness of the proposed method.
Rotation Invariant Texture Features and Their Use in Automatic Script Identification

Concerns the extraction of rotation invariant texture features and the use of such features in script identification from document images. Rotation invariant texture features are computed based on an extension of the popular multi-channel Gabor filtering technique, and their effectiveness is tested with 300 randomly rotated samples of 15 Brodatz textures. These features are then used in an attempt to solve a practical but hitherto mostly overlooked problem in document image processing-the identification of the script of a machine printed document. Automatic script and language recognition is an essential front-end process for the efficient and correct use of OCR and language translation products in a multilingual environment. Six languages (Chinese, English, Greek, Russian, Persian, and Malayalam) are chosen to demonstrate the potential of such a texture-based approach in script identification.
3D Model Based Vehicle Tracking Using Gradient Based Fitness Evaluation under Particle Filter Framework

We address the problem of 3D model based vehicle tracking from monocular videos of calibrated traffic scenes. A 3D wire-frame model is set up as prior information and an efficient fitness evaluation method based on image gradients is introduced to estimate the fitness score between the projection of vehicle model and image data, which is then combined into a particle filter based framework for robust vehicle tracking. Numerous experiments are conducted and experimental results demonstrate the effectiveness of our approach for accurate vehicle tracking and robustness to noise and occlusions.
Cast Shadow Removal Combining Local and Global Features

In this paper, we present a method using pixel-level information, local region-level information and global-level information to remove shadow. At the pixel-level, we employ GMM to model the behavior of cast shadow for every pixel in the HSV color space, as it can deal with complex illumination conditions. However, unlike the GMM for background which can obtain sample every frame, this model for shadow needs more frames to get the same number of sample, because shadow may not appear at the same pixel for each frame. Therefore, it will take a long time to converge. To overcome this drawback, we use the local region-level information to get more samples and global-level information to improve a preclassifier and then, by using it, we get samples which are more likely to be shadow. Also, at the local region-level, we use Markov random fields to represent dependencies between the label of single pixel and labels of its neighborhood. Moreover, to make global level information more robust, tracking information is used. Experimental results show that the proposed method is efficient and robust.
Content-based rotation-invariant image annotation

In this paper a semi-automatic approach towards efficient and flexible image database population is described. An interactive process aided by computer vision and image processing techniques is employed. Computer aided object selection, using region growing based on colour and texture, allows new labels to be added quickly and efficiently. Extracted features are used to propagate the label through the database. As the images are very unlikely to be acquired at the same viewpoint, the features should ideally be viewpoint invariant-an area which has been mostly overlooked despite its importance. This paper focuses on rotation invariance.
Margin Based Feature Selection for Cross-Sensor Iris Recognition via Linear Programming

The wide deployments of iris recognition systems promote the emergence of different types of iris sensors. Large differences such as illumination wavelength and resolution result in cross-sensor variations of iris texture patterns. These variations decrease the accuracy of iris recognition. To address this issue, a feasible solution is to select an optimal effective feature set for all types of iris sensors. In this paper, we propose a margin based feature selection method for cross-sensor iris recognition. This method learns coupled feature weighting factors by minimizing a cost function, which aims at selecting the feature set to represent the intrinsic characteristics of iris images from different sensors. Then, the optimization problem can be formulated and solved using linear programming. Extensive experiments on the Notre Dame Cross Sensor Iris Database and CASIA cross sensor iris database show that the proposed method outperforms conventional feature selection methods in cross-sensor iris recognition.
Cascading statistical and structural classifiers for iris recognition

Reliable human identification using iris pattern has recently gained growing interests from pattern recognition researchers. In literature of iris recognition, almost all algorithms are based on statistical information. In this paper, a structural iris image analysis method is proposed, which provides complementary information to statistical classifier. In order to save computational cost, the structural matcher is not consulted unless the statistical classifier is uncertain of its decision. At the second stage, the structural classifier may be combined with statistical classifier with different fusion strategies. The experimental results of decision-level classifiers combination are reported, which demonstrate that the cascaded classification system significantly outperforms single classifier.
Universal spatial feature set for video steganalysis

In this paper, we propose a universal spatial feature set for video steganalysis. This feature set comprehensively exploits the correlation of adjacent pixels and can be viewed as a generalized extension of most correlation based features. We also develop a new approach to extract inter-frame features for video steganalysis. Our method can be universally applied to detect different video steganographic algorithms regardless of video format. The experimental results show that it outperforms current correlation based methods.
Exploring DCT coefficient quantization effect for image tampering localization

In this paper, we focus on image tampering detection and tampered region localization. We find that the probability distributions of the DCT coefficients of a JEPG image will be influenced by tampering operation. Hence, we model the distributions of AC DCT coefficients of JPEG image and detect the tampered region from the unchanged region by using their different distributions. Based on an assumption of Laplacian distribution of unquantized AC DCT coefficients, Laplacian Mixture Model (LMM) is employed to model the quantized AC DCT coefficient distribution of a suspicious JPEG image. With the help of Expectation Maximization (EM) algorithm, the probability of an 8 × 8 block being tampered can be estimated; and then, a sophisticated image segmentation method, graph cut, is applied to determine the tampered region. Extensive experimental results on large scale databases prove the effectiveness of our proposed method which is suitable for different tampered region sizes at all levels including pixel, region and image level.
Cast Shadow Removal with GMM for Surface Reflectance Component

Cast shadow on the background is generated by an object moving between a light source and the background. The position and illumination of the source always change with time, while the background is stable. Therefore, features connected with light source always change with time, such as geometry and color. In this paper, we present a shadow removal method by homomorphic model to extract surface reflectance component, which is only connected with background of the scene and is robust to change of light source. We assume that reflectance component fits Gaussian distribution, and then use GMM to model it. Experimental results show that, except dealing with shadow, our method is not sensitive to the change of illumination
Detection of circular oil tanks based on the fusion of SAR and optical images

This paper aims to detect circular oil tanks automatically by using both SAR and optical images. The proposed method consists of two main steps: first, the bright areas are extracted from the SAR image to obtain the candidate regions for oil tanks; second, shape detection is performed in the optical image on these corresponding candidate areas, before we finally determine whether they are oil tanks or not. Prior knowledge, such as the validate dimensions of oil tanks and the group emerging property, is taken into account in this method. The experimental results demonstrate the effectiveness of the proposed method.
A behavior-based architecture for the control of an intelligent powered wheelchair

Autonomous robot architectures typically fall within two camps, top-down symbolic AI systems, and physically grounded bottom-up reactive systems. In this paper, we present a novel behavior fusion based architecture for the control of an intelligent powered wheelchair, which takes advantage of the modularity presented by the reactive bottom-up design, the inference mechanism of symbolic AI systems, and the flexibility of the layered architecture. Thus, this control architecture of wheelchair can realize the multi-level information fusion and facilitates task and information sharing and trading between man and machine. In addition, the basic behaviors and their fusion algorithm are also described.
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes

This paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. Six similarity measures are presented and the performance is evaluated by correct clustering rate (CCR) and time cost (TC). The experimental results demonstrate that in outdoor surveillance scenes, the simpler PCA+Euclidean distance is competent for the clustering task even in case of noise, as more complex similarity measures such as DTW, LCSS are not efficient due to their high computational cost
Orthogonal Diagonal Projections for Gait Recognition

Gait has received much attention from researchers in the vision field due to its utility in walker identification. One of the key issues in gait recognition is how to extract discriminative shape features from 2D human silhouette images. This paper deals with the problem of gait-based walker recognition using statistical shape features. First, we normalize walkers' silhouettes (to facilitate gait feature comparison) into a square form and use the orthogonal projections in the positive and negative diagonal directions to draw personal signatures contained in gait patterns. Then principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to reduce the dimensionality of original gait features and to improve the topological structure in the feature space. Finally, this paper accomplishes the recognition of unknown gait features based on the nearest neighbor rule, with the discussion of the effect of distance metrics and scales on discriminating performance. Experimental results justify the potential of our method.
A Novel Algorithm for View and Illumination Invariant Image Matching

The challenges in local-feature-based image matching are variations of view and illumination. Many methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors. However, the matching performance is still unstable and inaccurate, particularly when large variation in view or illumination occurs. In this paper, we propose a view and illumination invariant image-matching method. We iteratively estimate the relationship of the relative view and illumination of the images, transform the view of one image to the other, and normalize their illumination for accurate matching. Our method does not aim to increase the invariance of the detector but to improve the accuracy, stability, and reliability of the matching results. The performance of matching is significantly improved and is not affected by the changes of view and illumination in a valid range. The proposed method would fail when the initial view and illumination method fails, which gives us a new sight to evaluate the traditional detectors. We propose two novel indicators for detector evaluation, namely, valid angle and valid illumination, which reflect the maximum allowable change in view and illumination, respectively. Extensive experimental results show that our method improves the traditional detector significantly, even in large variations, and the two indicators are much more distinctive.
Statistics of local surface curvatures for mis-localized iris detection

Eye detection is a hot research topic in computer vision for its wide applications in human-computer interaction, face and iris recognition, etc. However, robust eye detection is still a grand challenge due to the numerous appearance variations of eye images in real-world applications. In this paper, we present a novel local surface curvature analysis method to deal with this problem. Firstly, by regarding an eye image as a 2D surface in 3D space, we propose to use the histogram of local surface curvatures as the general representation of eye pattern. Then, a SVM classifier is employed for eye detection using the histogram vectors of eye and non-eye samples. Extensive experiments are performed and the results show that the proposed method achieves state-of-the-art performance in eye detection. In particular, it is more efficient in mistakenly localized iris detection.
Adaptive Feature Selection via Boosting-Like Sparsity Regularization

In order to efficiently select a discriminative and complementary subset from a large feature pool, we propose a two-stage learning strategy considering both samples and their features simultaneously, namely sample selection and feature selection. The objective functions of both stages are consistent with a large margin loss. At the first stage, the support samples are selected by Support Vector Machine (SVM). At the second stage, a Boosting-like Sparsity Regularization (SRBoost) algorithm is presented to select a small number of complementary features. In detail, a weak learner is composed of a few features, which are selected by a sparsity enforcing mode, and an intermediate variable is gracefully used to reweight the corresponding sample. Extensive experimental results on the CASIA-IrisV4.0 database demonstrate that our method outperforms the state-of-the-art methods.
Robust view transformation model for gait recognition

Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods.
Joint Alignment and Clustering via Low-Rank Representation

Both image alignment and image clustering are widely researched with numerous applications in recent years. These two problems are traditionally studied separately. However in many real world applications, both alignment and clustering results are needed. Recent study has shown that alignment and clustering are two highly coupled problems. Thus we try to solve the two problems in a unified framework. In this paper, we propose a novel joint alignment and clustering algorithm by integrating spatial transformation parameters and clustering parameters into a unified objective function. The proposed function seeks the lowest rank representation among all the candidates that can represent misaligned images. It is indeed a transformed Low-Rank Representation. As far as we know, this is the first time to cluster the misaligned images using the transformed Low-Rank Representation. We can solve the proposed function by linear zing the objective function, and then iteratively solving a sequence of linear problems via the Augmented Lagrange Multipliers method. Experimental results on various data sets validate the effectiveness of our method.
Recovery of corrupted low-rank matrices via half-quadratic based nonconvex minimization

Recovering arbitrarily corrupted low-rank matrices arises in computer vision applications, including bioinformatic data analysis and visual tracking. The methods used involve minimizing a combination of nuclear norm and l1 norm. We show that by replacing the l1 norm on error items with nonconvex M-estimators, exact recovery of densely corrupted low-rank matrices is possible. The robustness of the proposed method is guaranteed by the M-estimator theory. The multiplicative form of half-quadratic optimization is used to simplify the nonconvex optimization problem so that it can be efficiently solved by iterative regularization scheme. Simulation results corroborate our claims and demonstrate the efficiency of our proposed method under tough conditions.
Efficient Night Gait Recognition Based on Template Matching

Gait is a useful biometric which can be used to recognize people at a distance when other biometrics are incapable. However, most work on gait recognition has been visible spectrum-oriented over the past decade, ignoring recognition at night which is in reality demand-imperative. This paper deals with the problem of night gait recognition via thermal infrared imagery. First of all, human detection is accomplished, based on the Gaussian mixture modeling of the background. Then, human silhouettes are extracted on the basis of preceding detection results. Moreover, a new gait representation called HTI is proposed to characterize gait signatures for recognition. An infrared night gait database was built to provide a foundation for night gait recognition. Experimental results on two gait datasets show the effectiveness of this method