nils-herrmann/clean_scirepeval huggingface dataset:
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs.

This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications.
DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification

MeshFace photos have been widely used in many Chinese business organizations to protect ID face photos from being misused. The occlusions incurred by random meshes severely degenerate the performance of face verification systems, which raises the MeshFace verification problem between MeshFace and daily photos. Previous methods cast this problem as a typical low-level vision problem, i.e., blind inpainting. They recover perceptually pleasing clear ID photos from MeshFaces by enforcing pixel level similarity between the recovered ID images and the ground-truth clear ID images and then perform face verification on them. Essentially, face verification is conducted on a compact feature space rather than the image pixel space. Therefore, this paper argues that pixel level similarity and feature level similarity jointly offer the key to improve the verification performance. Based on this insight, we offer a novel feature oriented blind face inpainting framework. Specifically, we implement this by establishing a novel DeMeshNet, which consists of three parts. The first part addresses blind inpainting of the MeshFaces by implicitly exploiting extra supervision from the occlusion position to enforce pixel level similarity. The second part explicitly enforces a feature level similarity in the compact feature space, which can explore informative supervision from the feature space to produce better inpainting results for verification. The last part copes with face alignment within the net via a customized spatial transformer module when extracting deep facial features. All three parts are implemented within an end-to-end network that facilitates efficient optimization. Extensive experiments on two MeshFace data sets demonstrate the effectiveness of the proposed DeMeshNet as well as the insight of this paper.
Multi-slice image texture edge detection by local vector mapping

A simple model for preattentive texture perception/discrimination is suggested. According to this model and with the introduction of local vector mapping (LVM) for texture description, a new algorithm for texture edge detection is developed. The algorithm basically consists of two steps, namely, intensity slicing to decompose complex forms into simple features, called textons, and textural transform to transform textural differences into gray-tone differences. Experimental results show that the algorithm has a number of desirable features such as the simplicity of the structures it uses, the consistency of the approach with the human visual system, and the accuracy of the detected texture edges.<<ETX>>
Salient coding for image classification

The codebook based (bag-of-words) model is a widely applied model for image classification. We analyze recent coding strategies in this model, and find that saliency is the fundamental characteristic of coding. The saliency in coding means that if a visual code is much closer to a descriptor than other codes, it will obtain a very strong response. The salient representation under maximum pooling operation leads to the state-of-the-art performance on many databases and competitions. However, most current coding schemes do not recognize the role of salient representation, so that they may lead to large deviations in representing local descriptors. In this paper, we propose “salient coding”, which employs the ratio between descriptors' nearest code and other codes to describe descriptors. This approach can guarantee salient representation without deviations. We study salient coding on two sets of image classification databases (15-Scenes and PASCAL VOC2007). The experimental results demonstrate that our approach outperforms all other coding methods in image classification.
An SVD-based watermarking scheme for protecting rightful ownership

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia documents in networked environments. There are two important issues that watermarking algorithms need to address. First, watermarking schemes are required to provide trustworthy evidence for protecting rightful ownership. Second, good watermarking schemes should satisfy the requirement of robustness and resist distortions due to common image manipulations (such as filtering, compression, etc.). In this paper, we propose a novel watermarking algorithm based on singular value decomposition (SVD). Analysis and experimental results show that the new watermarking method performs well in both security and robustness.
Boosting ordinal features for accurate and fast iris recognition

In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a dasiacascadepsila, by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-of-the-art iris recognition accuracy and speed. In addition, SOBoost outperforms Adaboost (Gentle-Adaboost, JS-Adaboost, etc.) in terms of both accuracy and generalization capability across different iris databases.
Coupled Topic Model for Collaborative Filtering With User-Generated Content

The user-generated content (UGC) is a type of dyadic information that provides description of the interaction between users and items (such as rating, purchasing, etc.). Most conventional methods incorporate either a user profile or the item description, which cannot well utilize this kind of content information. Some other works jointly consider user ratings and reviews, but they are based on the factorization technique and have difficulty in providing explanations on generated recommendations. In this study, a coupled topic model (CoTM) for recommendation with UGC is developed. By combining UGC and ratings, the method discussed in this study captures both the content-based preferences and collaborative preferences and, thus, can explain both the user and item latent spaces using the topics discovered from the UGC. The learned topics in CoTM can also serve as proper explanations for the generated recommendations. Experimental results show that the proposed CoTM model yields significant improvements over the compared competitive methods on two typical datasets, that is, MovieLens-10M and Citation-network V1. The topics discovered by CoTM can be used not only to illustrate the topic distributions of users and items, but also to explain the generated user-item recommendations.
SAR and SPOT Image Registration Based on Mutual Information with Contrast Measure

In this study, we propose a novel robust mutual information (MI) based method to register SAR and SPOT images. Traditional MI based method can register SAR and SPOT images well. However, its robustness is not satisfying for local spatial information is absent. In our approach, first, local contrast of 5*5 windows centered at each point in both images is calculated, then the contrast value is assigned to each pixel and two contrast images are obtained. Finally, the SAR and SPOT images are registered by maximizing the MI between their contrast images. Experimental results show that compared with traditional MI, our approach is much more robust and acquires comparable or even higher accuracy. Meanwhile, compared with the MI with orientation information based registration (MIOI), another robust MI based method, our algorithm works much faster and more accurately.
l2, 1 Regularized correntropy for robust feature selection

In this paper, we study the problem of robust feature extraction based on l2,1 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an l2,1-norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify l1-norm and l2,1-norm minimization within a common framework. In algorithmic part, we propose an l2,1 regularized correntropy algorithm to extract informative features meanwhile to remove outliers from training data. A new alternate minimization algorithm is also developed to optimize the non-convex correntropy objective. In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on largescale and open face recognition datasets.
A novel registration method for SAR and SPOT images

In this paper, we propose a novel mutual information (MI) based method to register SAR and SPOT images. The traditional MI can register SAR and SPOT images well. However, its robustness is weakened by the absence of orientation information. In our approach, we first extract orientation information at four directions by Gabor filters, then MI of each corresponding image pair is calculated and the average value of MI is used as an improved measure for MI. Experiments show that our method is more robust than the traditional MI method. Meanwhile our method maintains comparable accuracy to the traditional MI, which is much better than coarse manual registration.
Robust 3D Face Recognition Using Learned Visual Codebook

In this paper, we propose a novel learned visual code-book (LVC) for 3D face recognition. In our method, we first extract intrinsic discriminative information embedded in 3D faces using Gabor filters, then K-means clustering is adopted to learn the centers from the filter response vectors. We construct LVC by these learned centers. Finally we represent 3D faces based on LVC and achieve recognition using a nearest neighbor (NN) classifier. The novelty of this paper comes from 1) We first apply textons based methods into 3D face recognition; 2) We encompass the efficiency of Gabor features for face recognition and the robustness of texton strategy for texture classification simultaneously. Our experiments are based on two challenging databases, CASIA 3D face database and FRGC2.0 3D face database. Experimental results show LVC performs better than many commonly used methods.
Online Appearance Model Learning for Video-Based Face Recognition

In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper. First, we propose an algorithm which can learn appearance models online without using a pre-trained model. Second, we propose a method for eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.
A novel landmark tree based self-localization and path-planning method for an intelligent wheelchair

In this paper a novel approach of self-localization for an intelligent wheelchair is presented. Different from previous approaches we adopt a two-stage matching strategy to position control of an intelligent wheelchair, which is necessary if the current location is not known due to re-initialisation or an accident. Based on the landmark tree developed here, we combine a method similar to image-retrieval and a Bayes law based matching algorithm to locate the wheelchair in multiple resolutions, which is related to the requirement of task-completion. A new path-planning algorithm is introduced, which is originated from our landmark tree and is easy to use.
Feature Coding in Image Classification: A Comprehensive Study

Image classification is a hot topic in computer vision and pattern recognition. Feature coding, as a key component of image classification, has been widely studied over the past several years, and a number of coding algorithms have been proposed. However, there is no comprehensive study concerning the connections between different coding methods, especially how they have evolved. In this paper, we first make a survey on various feature coding methods, including their motivations and mathematical representations, and then exploit their relations, based on which a taxonomy is proposed to reveal their evolution. Further, we summarize the main characteristics of current algorithms, each of which is shared by several coding strategies. Finally, we choose several representatives from different kinds of coding approaches and empirically evaluate them with respect to the size of the codebook and the number of training samples on several widely used databases (15-Scenes, Caltech-256, PASCAL VOC07, and SUN397). Experimental findings firmly justify our theoretical analysis, which is expected to benefit both practical applications and future research.
Human Activity Recognition Based on R Transform

This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The advantage of the R transform lies in its low computational complexity and geometric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, R transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in recognizing similar activities. Rich experiments have proved the efficiency of the proposed method.
Personal Identification Based on Iris Texture Analysis

With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.
Histograms of Gabor Ordinal Measures for face representation and recognition

This paper proposes a new image representation method named Histograms of Gabor Ordinal Measures (HOGOM) for robust face recognition. First, a novel texture descriptor, Gabor Ordinal Measures (GOM), is developed to inherit the advantages from Gabor features and Ordinal Measures. GOM applies Gabor filters of different orientations and scales on the face image and then computes Ordinal Measures over each Gabor magnitude response. Second, in order to obtain an effective and compact representation, the binary values of each GOM, for different orientations at a given scale, are encoded into a single decimal number and then spatial histograms of non-overlapping rectangular regions are computed. Finally, a nearest-neighbor classifier with the χ2 dissimilarity measure is used for classification. HOGOM has three principal advantages: 1) it succeeds the spatial locality and orientation selectivity from Gabor features; 2) the adopted region-comparison strategy makes it more robust; 3) by applying the binary codification and computing spatial histograms, it becomes more stable and efficient. Extensive experiments on the large-scale FERET database and AR database show the robustness of the proposed descriptor, achieving the state of the art.
Evaluation of feature sets for steganalysis of JPEG image

In past several years so many feature sets for steganalysis were proposed to detect stego images. These features based on different ideas and were considered to be effective for most steganography schemes. However a systematically comparison of these features have not been made in previous papers. In order to get a view of performance of current features in state of art, we designed several experiment to make a evaluation of them. Experiment result and conclusions draw from it were proposed in this paper.
Key Techniques and Methods for Imaging Iris in Focus

Automated iris recognition is a promising method for noninvasive verification of identity. How to acquire an iris image in focus is a key issue in iris recognition. Based on imaging properties of a simple lens, working principles of fixed focus and auto focus imaging systems are described. Key techniques for imaging iris in focus are discussed in this paper, such as illumination, lens design and self alignment to position the iris in the system's depth of field. According to the techniques, a clear iris acquisition system is developed and experimental results are presented in this paper
Combine hierarchical appearance statistics for accurate palmprint recognition

Palmprint recognition is an active member of biometrics in recent years. State-of-the-art algorithms of palmprint recognition describe appearances of palmprints efficiently through local texture analysis. Following this framework, we propose a novel approach of palmprint recognition in this paper, which represents palmprint images based on statistics and spatial arrangement of appearance descriptors within local image areas. In this method, we firstly design a robust descriptor to encode properties of palmprint appearances of local regions. The whole image is divided into non-overlapped blocks at increasingly fine resolutions successively, so as to describe the spatial layout in hierarchical scales. For a specific spatial resolution, local distributions of the proposed descriptors in the blocks are concatenated to represent structures of palmprint structures. Finally, distribution information of different resolutions is combined to provide complementary descriptive power. Promising experimental results demonstrate that the proposed method achieves even better performances than the state-of-the-art approaches.
Fingerprint matching based on error propagation

Fingerprint matching is one of the most important modules in automatic fingerprint identification systems (AFIS). Traditional methods treat this problem as point pattern matching, which is essentially an intractable problem. We propose an effective fingerprint matching algorithm based on error propagation. First, we find initial correspondence according to the ridge information associated with each minutia. Secondly, we construct a MatchedSet which is composed of initial correspondence and the surrounding matched pairs. Finally, the matching errors of the minutiae pairs in the MatchedSet are adopted to guide the subsequent matching process and the newly matched pairs are added into the MatchedSet. The operation is repeated until the elements in the MatchedSet do not change any more. In this procedure, the local deformation can be tracked, thus the algorithm provides robustness to non-linear deformation commonly seen in fingerprint images. The algorithm was tested on NIST-24 database, and the result is promising.
Head pose localization based on multicue fusion*

User's face localization and head pose estimation play an essential role in the application of face-to-face human-computer interactive systems. In this paper, we formulate pose localization in a Bayesian framework by particle sampling. Proposal distribution fusing multiple cues such as color, shape and motion is defined to realize pose localization and tracking, which effectively enhances the performance of particle sampling. Experiment results on static images and real videos show the efficiency of our algorithm.