nils-herrmann/clean_scirepeval huggingface dataset:
Exploring complementary features for iris recognition on mobile devices

Iris recognition on mobile devices is challenging due to a large number of low-quality iris images acquired in complex imaging conditions. Illumination variations, low resolution and serious noises reduce the distinctiveness of iris texture. This paper explores complementary features to improve the accuracy of iris recognition on mobile devices. Firstly, optimized ordinal measures (OMs) features are extracted to encode local iris texture. Afterwards, pairwise features are automatically learned to measure the correlation between two irises using the convolutional neural network (CNN). Finally, the selected OMs features and the learned pairwise features are fused at the score level. Experiments are performed on a newly constructed mobile iris database which contains 6000 images of 200 Asian subjects. Their iris images of left and right eyes are obtained simultaneously at varying standoff distances. Experimental results demonstrate OMs features and pairwise features are highly complementary and effective for iris recognition on mobile devices.
Writer identification based on handwriting

This paper describes a text-independent writer identification method. The difficulties with writer identification are discussed. These include the sensitivity of the identification algorithm to variations in the size of the training samples, in the words, line and character spacing, point sizes, and scanner resolutions. The work described demonstrates that texture analysis is a useful tool for writer identification based on handwriting. We use multichannel spatial filtering techniques to extract texture features from a nonuniformly skewed and nonskewed handwriting image. There are many available tilters in the multichannel technique. We use Gabor filters, since they have proven to be successful in extracting features for similar applications. We also use grey-scale co-occurrence matrices (GSCM) for feature extraction (for comparison purposes). Two classification techniques are adopted here, namely the weighted Euclidean distance (WED) and the k-NN classifiers. Our algorithm achieves a classification accuracy of 95.3% using 300 test images from 20 writers.
Uniform low-rank representation for unsupervised visual domain adaptation

Visual domain adaptation aims to adapt a model learned in source domain to target domain, which has received much attention in recent years. In this paper, we propose a uniform low-rank representation based unsupervised domain adaptation method which captures the intrinsic relationship among the source and target samples and meanwhile eliminates the disturbance from the noises and outliers. In particular, we first align the source and target samples into a common subspace using a subspace alignment technique. Then we learn a domain-invariant dictionary with respect to the transformed source and target samples. Finally, all the transformed samples are low-rank represented based on the learned dictionary. Extensive experimental results show that our method is beneficial to reducing the domain difference, and we achieve the state-of-the-art performance on the widely used visual domain adaptation benchmark.
Counterfeit iris detection based on texture analysis

This paper addresses the issue of counterfeit iris detection, which is a liveness detection problem in biometrics. Fake iris mentioned here refers to iris wearing color contact lens with textures printed onto them. We propose three measures to detect fake iris: measuring iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of iris textures and using selected features based on co-occurrence matrix (CM). Extensive testing is carried out on two datasets containing different types of contact lens with totally 640 fake iris images, which demonstrates that Iris-Texton and CM features are effective and robust in anticounterfeit iris. Detailed comparisons with two state-of-the-art methods are also presented, showing that the proposed iris edge sharpness measure acquires a comparable performance with these two methods, while Iris-Texton and CM features outperform the state-of-the-art.
Dynamic Audio-Visual Mapping using Fused Hidden Markov Model Inversion Method

Realistic audio-visual mapping remains a very challenging problem. Having short time delay between inputs and outputs is also of great importance. In this paper, we present a new dynamic audio-visual mapping approach based on the Fused Hidden Markov Model Inversion method. In our work, the Fused HMM is used to model the loose synchronization nature of the two tightly coupled audio speech and visual speech streams explicitly. Given novel audio inputs, the inversion algorithm is derived to synthesize visual counterparts by maximizing the joint probabilistic distribution of the Fused HMM. When it is implemented in the subsets built from the training corpus, realistic synthesized facial animation having relative short time delay is obtained. Experiments on a 3D motion capture bimodal database show that the synthetic results are comparable with the ground truth.
Exploring relations of visual codes for image classification

The classic Bag-of-Features (BOF) model and its extensional work use a single value to represent a visual code. This strategy ignores the relation of visual codes. In this paper, we explore this relation and propose a new algorithm for image classification. It consists of two main parts: 1) construct the codebook graph wherein a visual code is linked with other codes; 2) describe each local feature using a pair of related codes, corresponding to an edge of the graph. Our approach contains richer information than previous BOF models. Moreover, we demonstrate that these models are special cases of ours. Various coding and pooling algorithms can be embedded into our framework to obtain better performance. Experiments on different kinds of image classification databases demonstrate that our approach can stably achieve excellent performance compared with various BOF models.
Efficient image gradient-based object localisation and recognition

This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilineal: The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles demonstrates the efficiency and robustness of context-based computer vision in road traffic scenes. The limitations of the algorithms are also addressed in the paper.
Fast Principal Component Analysis using Eigenspace Merging

In this paper, we propose a fast algorithm for principal component analysis (PCA) dealing with large high-dimensional data sets. A large data set is firstly divided into several small data sets. Then, the traditional PCA method is applied on each small data set and several eigenspace models are obtained, where each eigenspace model is computed from a small data set. At last, these eigenspace models are merged into one eigenspace model which contains the PCA result of the original data set. Experiments on the FERET data set show that this algorithm is much faster than the traditional PCA method, while the principal components and the reconstruction errors are almost the same as that given by the traditional method.
A compact optical flowbased motion representation for real-time action recognition in surveillance scenes

We address the problem of action recognition. Our aim is to recognize single person activities in surveillance scenes. To meet the requirements of real scene action recognition, we present a compact motion representation for human activity recognition. With the employment of efficient features extracted from optical flow as the main part, together with global information, our motion representation is compact and discriminative. We also build a novel human action dataset(CASIA) in surveillance scene with three vertically different viewpoints and distant people. Experiments on CASIA dataset and WEIZMANN dataset show that our method can achieve satisfying recognition performance with low computational cost as well as robustness against both horizontal(panning) and vertical(tilting) viewpoint changes.
Fusion of static and dynamic body biometrics for gait recognition

Vision-based human identification at a distance has recently gained growing interest from computer vision researchers. This paper describes a human recognition algorithm by combining static and dynamic body biometrics. For each sequence involving a walker, temporal pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. In addition, a model-based approach is presented under a Condensation framework to track the walker and to further recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues obtained from walking video may be independently used for recognition using the nearest exemplar classifier. They are fused on the decision level using different combinations of rules to improve the performance of both identification and verification. Experimental results of a dataset including 20 subjects demonstrate the feasibility of the proposed algorithm.
Semi-supervised Learning for RGB-D Object Recognition

Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images, (2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning framework based on RGB and depth (RGB-D) images to improve object recognition. In particular, our framework has two modules: (1) RGB and depth images are represented by convolutional-recursive neural networks to construct high level features, respectively, (2) co-training is exploited to make full use of unlabeled RGB-D instances due to the existing two independent views. Experiments on the standard RGB-D object dataset demonstrate that our method can compete against with other state-of-the-art methods with only 20% labeled data.
Combining null space-based Gabor features for face recognition

We propose a face recognition strategy combining various discriminating Gabor features in multi-scales and multi-orientations. A bank of well-chosen Gabor filters is applied on the image to construct a group of feature vectors, and then the Null Space-based LDA (NLDA) is performed simultaneously on each orientation channel and the original image to give 5 component classifier outputs, which are then combined to increase the final recognition rate. Experimental results on the FERET database demonstrate the effectiveness and flexibility of our proposed method.
Comprehensive assessment of iris image quality

Iris image quality critically determines iris recognition performance and the quality metrics of iris images are also useful prior information for adaptive selection of optimal recognition strategy. Iris image quality is jointly determined by multiple factors such as focus, occlusion, off-angle, deformation, etc. So it is a complex problem to assess the overall quality score of an iris image. This paper proposes a novel framework for comprehensive assessment of iris image quality. The contributions of the paper include three aspects: (i) Three novel approaches are proposed to estimate the quality metrics (QM) of defocus, motion blur and off-angle in an iris image respectively, (ii) A fusion method based on likelihood ratio is proposed to combine six quality factors of an iris image into an unified quality score. (iii) A statistical quantization method based on t-test is proposed to adaptively classify the iris images in a database into a number of quality levels. Extensive experiments demonstrate the proposed framework can effectively assess the overall quality of iris images. And the relationship between iris recognition results and the quality level of iris images can be explicitly formulated.
Adaptive multi-resolution fitting and its application to realistic head modeling

The general approach for object modeling is to construct the surface from the high-quality range points obtained from laser scanners. In this paper, we face the noise point cloud obtained from image sequences by a common camera and develop a novel algorithm of adaptive multi-resolution fitting (AMRF) for object modeling. This algorithm combines the adaptive subdivision scheme with multi-resolution fitting so that the control model is subdivided locally and adaptively according to the local complexity of the point cloud and approximates the 3D data level by level. The proposed method can conquer the holes and outliers efficiently and create full compatibility between the complexity of the mesh model and the representation of the local details. We apply the proposed method to the complete head modeling with the real data, and the results seem very promising.
Semi-supervised subspace segmentation

Subspace segmentation methods usually rely on the raw explicit feature vectors in an unsupervised manner. In many applications, it is cheap to obtain some pairwise link information that tells whether two data points are in the same subspace or not. Though partially available, such link information serves as some kind of high-level semantics, which can be further used as a constraint to improve the segmentation accuracy. By constructing a link matrix and using it as a regularizer, we propose a semi-supervised subspace segmentation model where the partially observed subspace membership prior can be encoded. Specificly, under the common linear representation assumption, we enforce the representational coefficient to be consistent with the link matrix. Thus the low-level and high-level information about the data can be integrated to produce more precise segmentation results. We then develop an effective algorithm to optimize our model in an alternating minimization way. Experimental results for both motion segmentation and face clustering validate that incorporating such link information is helpful to assist and bias the unsupervised subspace segmentation methods.
See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-identification

Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. This topic has gained increasing interests in computer vision recently. However, less attention has been paid to video-based approaches, compared with image-based ones. Two steps are usually involved in previous approaches, namely feature learning and metric learning. But most of the existing approaches only focus on either feature learning or metric learning. Meanwhile, many of them do not take full use of the temporal and spatial information. In this paper, we concentrate on video-based person re-identification and build an end-to-end deep neural network architecture to jointly learn features and metrics. The proposed method can automatically pick out the most discriminative frames in a given video by a temporal attention model. Moreover, it integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video. That is, our method handles spatial and temporal information simultaneously in a unified manner. The carefully designed experiments on three public datasets show the effectiveness of each component of the proposed deep network, performing better in comparison with the state-of-the-art methods.
Extended optimization method of LSB steganalysis

Image steganalysis has attracted increasing attention recently. LSB steganalysis is one of the most active research topics. The paper proposes a method for LSB steganalysis of images, where the secret message is embedded in a given number L of the least significant bits. The proposed estimation method is an extension of Fridrich's method from the case L = 1 to arbitrary L > 0. A weighted stego image is defined first and then estimation formula is derived. To evaluate the proposed steganalytic method, two experiments of detection and estimation are performed. It is shown that the accuracy of detecting the existence of secret messages in images and of estimating the embedding ratio of secret messages is relatively high. Estimation errors and further studies are also discussed. Experimental results and theoretical verification show that this method is an effective method of LSB steganalysis.
Articulated model based people tracking using motion models

This paper focuses on acquisition of human motion data such as joint angles and velocity for applications of virtual reality, using both an articulated body model and a motion model in the CONDENSATION framework. Firstly, we learn a motion model represented by Gaussian distributions, and explore motion constraints by considering the dependency of motion parameters and represent them as conditional distributions. Both are integrated into the dynamic model to concentrate factored sampling in the areas of state-space with most posterior information. To measure the observing density with accuracy and robustness, a PEF (pose evaluation function) modeled with a radial term is proposed. We also address the issue of automatic acquisition of initial model posture and recovery from severe failures. A large number of experiments on several persons demonstrate that our approach works well.
Accurate iris segmentation in non-cooperative environments using fully convolutional networks

Conventional iris recognition requires controlled conditions (e.g., close acquisition distance and stop-and-stare scheme) and high user cooperation for image acquisition. Non-cooperative acquisition environments introduce many adverse factors such as blur, off-axis, occlusions and specular reflections, which challenge existing iris segmentation approaches. In this paper, we present two iris segmentation models, namely hierarchical convolutional neural networks (HCNNs) and multi-scale fully convolutional network (MFCNs), for noisy iris images acquired at-a-distance and on-the-move. Both models automatically locate iris pixels without handcrafted features or rules. Moreover, the features and classifiers are jointly optimized. They are end-to-end models which require no further pre- and post-processing and outperform other state-of-the-art methods. Compared with HCNNs, MFCNs take input of arbitrary size and produces correspondingly-sized output without sliding window prediction, which makes MFCNs more efficient. The shallow, fine layers and deep, global layers are combined in MFCNs to capture both the texture details and global structure of iris patterns. Experimental results show that MFCNs are more robust than HCNNs to noises, and can greatly improve the current state-of-the-arts by 25.62% and 13.24% on the UBIRIS.v2 and CASIA.v4-distance databases, respectively.
Human appearance matching across multiple non-overlapping cameras

In this paper, we present a new solution to the problem of appearance matching across multiple non-overlapping cameras. Objects of interest, pedestrians are represented by a set of region signatures centered at points sampled from edges. The problem of frame-to-frame appearance matching is formulated as finding corresponding points in two images as minimization of a cost function over the space of correspondence. The correspondence problem is solved under integer optimization framework where the cost function is determined by similarity of region signatures as well as geometric constraints between points. Experimental results demonstrate the effectiveness of the proposed method.
Efficient Object Tracking by Incremental Self-Tuning Particle Filtering on the Affine Group

We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group, which can find the optimal state in a chainlike way with a very small number of particles. Unlike traditional particle filtering, which only relies on random sampling for state optimization, ISPF incrementally draws particles and utilizes an online-learned pose estimator (PE) to iteratively tune them to their neighboring best states according to some feedback appearance-similarity scores. Sampling is terminated if the maximum similarity of all tuned particles satisfies a target-patch similarity distribution modeled online or if the permitted maximum number of particles is reached. With the help of the learned PE and some appearance-similarity feedback scores, particles in ISPF become “smart” and can automatically move toward the correct directions; thus, sparse sampling is possible. The optimal state can be efficiently found in a step-by-step way in which some particles serve as bridge nodes to help others to reach the optimal state. In addition to the single-target scenario, the “smart” particle idea is also extended into a multitarget tracking problem. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of particles.
Recognition of motion blurred iris images

It is inevitable to capture a portion of iris images with motion blur during iris recognition. The texture details on iris patterns are lost in motion blurred images so it may cause recognition performance degradation. This paper presents a first systematic study on the issue of motion blurred iris image recognition. Firstly, the reason of generating motion blurred iris images is analyzed. Secondly, the influence of the strength and the direction of motion blur on the accuracy of iris recognition is quantitatively investigated. Thirdly, we propose two solutions which can be used separately or jointly to improve recognition accuracy on motion blurred iris images. The first solution is a deblur-ring method in preprocessing stage and the other is a motion blur weight map with two generation methods in matching stage. Experimental results on both synthetic and real-world motion blurred iris image databases demonstrate the effectiveness and efficiency of our methods.
Robust object tracking via online learning of adaptive appearance manifold

Appearance modeling plays a critical role in robust object tracking, which should be adaptive to various appearance changes. We propose a new appearance model based on adaptive appearance manifold for object tracking. The adaptive appearance manifold consists of several submanifolds and each is approximated with a low dimensional linear subspace. The initial appearance model is constructed using location information of target object in the first frame, and no prior knowledge is needed. We design an efficient dynamic structure for the adaptive appearance manifold, which can reduce time of comparison between a new observation and the appearance model. The appearance model is incrementally learned online using the input sequence image. We integrate our new appearance model with the particle filtering framework. Several public challenging videos are used to test our tracking algorithm. The experimental results demonstrate that our algorithm is robust to illumination change, pose variation, partial occlusion and clutter background. And the speed of our algorithm is also very fast.