nils-herrmann/clean_scirepeval huggingface dataset:
3D model based vehicle localization by optimizing local gradient based fitness evaluation

We address the problem of 3D model based vehicle localization in calibrated traffic scenes. A wire-frame vehicle model is set up as prior information and an efficient local gradient based method is proposed to evaluate the fitness between the projection of 3D model and image data, which illustrates smooth optimization surface and more conspicuous peak with low computational cost. Gradient decent is then applied to optimize the evaluation score for localization. Experimental results demonstrate the accuracy, efficiency and robustness of the proposed method for model based vehicle localization.
Predict and improve iris recognition performance based on pairwise image quality assessment

The iris recognition performance is partially dependent on the relative quality variations of pairwise iris images. So bridging the gap between the quality and the matching score of pairwise iris images is helpful to predict and improve iris recognition performance. This paper formulates the relationship between matching score and quality of pairwise iris images as a statistical regression problem. Firstly, a number of quality measures of iris images such as focus, motion blur, illumination, off-angle, occlusions and dilation are computed as the performance related feature vector of iris images. And then partial least squares regression is used to establish two models to predict the intra score and inter score from pairwise iris image quality respectively. Finally, we define the uncertainty interval of matching scores. The uncertain match pairs are discarded to improve the recognition performance. Extensive experiments on ICE 1.0, CASIA-Iris-Lamp and CASIA-Iris-Thousand demonstrate that the proposed method can accurately estimate the distributions of matching scores. It can simultaneously improve the performance, even using simple features in recognition.
Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers

Biometrics is the technique of automatically recognizing individuals based on their biological or behavioral characteristics. Various biometric traits have been introduced and widely investigated, including fingerprint, iris, face, voice, palmprint, gait and so forth. Apart from identity, biometric data may convey various other personal information, covering affect, age, gender, race, accent, handedness, height, weight, etc. Among these, analysis of demographics (age, gender, and race) has received tremendous attention owing to its wide real-world applications, with significant efforts devoted and great progress achieved. This survey first presents biometric demographic analysis from the standpoint of human perception, then provides a comprehensive overview of state-of-the-art advances in automated estimation from both academia and industry. Despite these advances, a number of challenging issues continue to inhibit its full potential. We second discuss these open problems, and finally provide an outlook into the future of this very active field of research by sharing some promising opportunities.
Robust iris segmentation based on learned boundary detectors

Iris segmentation aims to isolate the valid iris texture regions useful for personal identification from the background of an iris image. Most state-of-the-art iris segmentation methods are based on edge information. However, generic edge detection methods may generate a large number of noisy edge points which can mislead iris localization. Therefore a robust iris segmentation method based on specific edge detectors is proposed in this paper. Firstly, a set of visual features including intensity, gradient, texture and structure information is used to characterize the edge points on iris boundaries. Secondly, AdaBoost is employed to learn six class-specific boundary detectors for localization of left/right pupillary boundaries, left/right limbic boundaries and upper/lower eyelids respectively. Thirdly, inner and outer boundaries of the iris ring are localized using weighted Hough transforms based on the output of the corresponding detectors. Finally, the edge points on the eyelids are detected and fitted as parabolas by robust least squares fitting. Extensive experiments on the challenging CASIA-Iris-Thousand iris image database demonstrate the effectiveness of the proposed iris segmentation method.
Blind Image Steganalysis Based on Statistical Analysis of Empirical Matrix

In this paper, a novel steganalysis method based on statistical analysis of empirical matrix (EM) is proposed to detect the presence of hidden message in an image. The projection histogram (PH) of EM is used to extract features composed of two parts: the moments of PH and the moments of the characteristic function of PH. Also, features extracted from prediction-error image by Yun Q. Shi et al, (2005) are included to enhance performance. SVM is utilized as classifier. A test database is constructed, based on which a detailed test for different categories of features and a comparison with methods in prior arts are conducted. Experiments show that the features we proposed are more effective than prior arts and our steganalysis method could blindly detect the presence of data hiding for various embedding schemes with high performance
Fast Supervised Discrete Hashing

Learning-based hashing algorithms are “hot topics” because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called “fast supervised discrete hashing” (FSDH) based on “supervised discrete hashing” (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.
Fusion of global and local features for face verification

A personalized feature combination scheme is proposed for face verification. ANFIS (adaptive neuro-fuzzy inference system) and SVM (support vector machine) are adopted respectively to form specialized feature representation for each subject by fusing global and local features. Instead of the common way for different subjects, we realize a new representation that adapts to each individual. Such adaptability in feature selection is inspired by face recognition in the HVS (human visual system) and results in an improved recognition rate.
An effective image steganalysis method based on neighborhood information of pixels

This paper focuses on image steganalysis. We use higher order image statistics based on neighborhood information of pixels (NIP) to detect the stego images from original ones. We use subtracting gray values of adjacent pixels to capture neighborhood information, and also make use of “rotation invariant” property to reduce the dimensionality for the whole feature sets. We tested two kinds of NIP feature, the experimental results illustrates that our proposed feature sets are with good performance and even outperform the state-of-art in certain aspect.
Real-Time Moving Object Classification with Automatic Scene Division

We address the problem of moving object classification. Our aim is to classify moving objects of traffic scene videos into pedestrians, bicycles and vehicles. Instead of supervised learning and manual labeling of large training samples, our classifiers are initialized and refined online automatically. With efficient features extracted and organized, the approach can be real-time and achieve high classification accuracy. Once the view or scene changes detected, the algorithm can automatically refine the classifiers and adapt them to new environments. Experimental results demonstrate the effectiveness and robustness of the proposed approach.
Robust moving object segmentation with two-stage optimization

Inspired by interactive segmentation algorithms, we propose an online and unsupervised technique to extract moving objects from videos captured by stationary cameras. Our method consists of two main optimization steps, from local optimal extraction to global optimal segmentation. In the first stage, reliable foreground and background pixels are extracted from input image by modeling distributions of foreground and background with color and motion cues. These reliable pixels provide hard constraints for the next step of segmentation. Then global optimal segmentation of moving object is implemented by graph cuts in the second stage. Experimental results on several challenging videos demonstrate the effectiveness and robustness of the proposed approach.
Introduction to the Special Issue on Biometrics: Progress and Directions

The guest editors provide an overview of the articles selected for this special issue. The issue's goal is to document the current state-of-the-art, acknowledge the latest breakthroughs achieved by scientists working in the area of biometric recognition, and identify future promising research areas. It is thought the selection of papers discussed should give readers a good idea of where researchers have been focusing, both on long- studied problems still needing more work and on newer challenges. A fundamental of the field of biometrics is an ever-increasing need for better recognition and stronger security. But, as public and commercial biometric deployments increase in number, there is also more need to understand privacy issues and to provide greater ease-of-use. The volume and quality of papers in this special issue indicate that much progress has been made in many aspects of the biometrics field and that there are challenging and promising future directions still to follow.
Visual tracking via 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. SIFT (Scale Invariant Feature Transform) like descriptors are used as basic features, and IPCA (Incremental Principle Component Analysis) is utilized to learn an adaptive appearance subspace for similarity measurement. ISPF tries to find the optimal target position in a step-by-step way: particles are incrementally drawn and intelligently tuned to their best states by an online LWPR (Local Weighted Projection Regression) pose estimator; searching is terminated if the maximum similarity of all tuned particles satisfies a target similarity distribution (TSD) modeled online or the permitted maximum number of particles is reached. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of random particles.
Recovering the Topology of Multiple Cameras by Finding Continuous Paths in a Trellis

In this paper, we propose an unsupervised method for recovering the topology of multiple cameras with non-overlapping fields of view. The nodes in the topology graph are defined as entry/exit zones in each camera while the connectivity between nodes is inferred through finding continuous paths in a trellis where appearance information and temporal information of moving objects are encoded. Unlike previous methods which assume a single mode transition distribution between nodes, our method is capable of dealing with multi-modal transition situations when both cars and pedestrians are in the scene. Results on simulated and real-life datasets demonstrate the effectiveness of the proposed method.
Enhanced Biologically Inspired Model for Object Recognition.

The biologically inspired model (BIM) proposed by Serre presents a promising solution to object categorization. It emulates the process of object recognition in primates' visual cortex by constructing a set of scale- and position-tolerant features whose properties are similar to those of the cells along the ventral stream of visual cortex. However, BIM has potential to be further improved in two aspects: mismatch by dense input and randomly feature selection due to the feedforward framework. To solve or alleviate these limitations, we develop an enhanced BIM (EBIM) in terms of the following two aspects: 1) removing uninformative inputs by imposing sparsity constraints, 2) apply a feedback loop to middle level feature selection. Each aspect is motivated by relevant psychophysical research findings. To show the effectiveness of the EBIM, we apply it to object categorization and conduct empirical studies on four computer vision data sets. Experimental results demonstrate that the EBIM outperforms the BIM and is comparable to state-of-the-art approaches in terms of accuracy. Moreover, the new system is about 20 times faster than the BIM.
A Semi-Supervised Method for Surveillance-Based Visual Location Recognition

In this paper, we are devoted to solving the problem of crossing surveillance and mobile phone visual location recognition, especially for the case that the query and reference images are captured by mobile phone and surveillance camera, respectively. Besides, we also study the influence of the environmental condition variations on this problem. To explore that problem, we first build a cross-device location recognition dataset, which includes images of 22 locations taken by mobile phones and surveillance cameras under different time and weather conditions. Then based on careful analysis of the problems existing in the data, we specifically design a method which unifies an unsupervised subspace alignment method and the semi-supervised Laplacian support vector machine. Experiments are performed on our dataset. Compared with several related methods, our method shows to be more efficient on the problem of crossing surveillance and mobile phone visual location recognition. Furthermore, the influence of several factors such as feature, time, and weather is studied.
Texture feature extraction based on primitive analysis

The authors examine feature extraction from textures with weakly coupled primitives. Three simple techniques are presented for primitive extraction. Texture features are defined as means and standard deviations of the first-order distributions of primitive attributes such as area, perimeter, compactness, orientation, eccentricity, color, and contrast. The effectiveness of the extracted texture features is illustrated in the context of texture classification. Experimental results show that the extracted texture features are particularly effective for macro- and regular texture discrimination but not so effective for micro- and more random textures.<<ETX>>
Supervised Discrete Hashing With Relaxation

Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called “supervised discrete hashing with relaxation” (SDHR) based on “supervised discrete hashing” (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.
Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation

Unsupervised domain adaptation aims to leverage the labeled source data to learn with the unlabeled target data. Previous trandusctive methods tackle it by iteratively seeking a low-dimensional projection to extract the invariant features and obtaining the pseudo target labels via building a classifier on source data. However, they merely concentrate on minimizing the cross-domain distribution divergence, while ignoring the intra-domain structure especially for the target domain. Even after projection, possible risk factors like imbalanced data distribution may still hinder the performance of target label inference. In this paper, we propose a simple yet effective domain-invariant projection ensemble approach to tackle these two issues together. Specifically, we seek the optimal projection via a novel relaxed domain-irrelevant clustering-promoting term that jointly bridges the cross-domain semantic gap and increases the intra-class compactness in both domains. To further enhance the target label inference, we first develop a ‘sampling-and-fusion’ framework, under which multiple projections are independently learned based on various randomized coupled domain subsets. Subsequently, aggregating models such as majority voting are utilized to leverage multiple projections and classify unlabeled target data. Extensive experimental results on six visual benchmarks including object, face, and digit images, demonstrate that the proposed methods gain remarkable margins over state-of-the-art unsupervised domain adaptation methods.
Model-based tracking of human walking in monocular image sequences

We present a simple but effective method for recovering a 3D human body, walking parallel to the image plane from monocular video sequences based on a 3D human body model, using robust image matching and a specific search strategy. Monocular 3D body tracking is challenging, because of a large number of free parameters. These parameters are reduced to 12 under the assumption of walking parallel to the image plane. To effectively measure the matching error a pose evaluation function combining both boundary and region information is provided. We simultaneously calculate the physical forces that are used as heuristic information in subsequent search. Experiments with both indoor and outdoor scenes demonstrate that our approach has an encouraging performance.
LivDet iris 2017 — Iris liveness detection competition 2017

Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions.
Ontology-based hierarchical conceptual model for semantic representation of events in dynamic scenes

There is an increasing interest in semantic analysis of events in dynamic scenes in recent years, and many different methods have been reported for this challenging problem. A new approach towards event modeling and analysis with semantic representations is proposed in this paper. Our method is inspired by the entity-relation model in software engineering. It integrates all related information into a hierarchical conceptual model by the name of ontology, and defines events as significant changes and mappings of conceptual units in the mode. All concepts are represented by three basic components, an entity, a word, and a set of attributes. The lower level of our framework achieves the task of feature extraction, and in the upper level, semantically meaningful representations of events are received by using these words. So our framework is data-driven and provides semantic outputs. Semantic similarity measurement of concepts is another important problem. In this paper we propose a method that uses conceptual status vector (CSV) and weighted semantic distance (WSD) to deal with it. Experimental results are presented which demonstrate the effectiveness of our approach on real-world videos captured from different scenes.
Coupled feature selection for cross-sensor iris recognition

It is necessary to match heterogeneous iris images captured by different types of iris sensors with an increasing demand of interoperable identity management systems. The significant differences among multiple types of iris sensors such as optical lens and illumination wavelength determine the cross-sensor variations of iris texture patterns. Therefore it is a challenging problem to select the common feature set which is effective for all types of iris sensors. This paper proposes a novel optimization model of coupled feature selection for cross-sensor iris recognition. The objective function of our model includes two parts: the first part aims to minimize the misclassification errors; the second part is designed to achieve sparsity in coupled feature spaces based on l2,1-norm regularization. In the training stage, the proposed feature selection model can be formulated as a half-quadratic optimization problem, where an iterative algorithm is developed to obtain the solution. Experimental results on the Notre Dame Cross Sensor Iris Database and CASIA cross sensor iris database show that features selected by the proposed method perform better than those selected by conventional single-space feature selection methods such as Boosting and h regularization methods.
Automatic 3D face recognition combining global geometric features with local shape variation information

Face recognition is a focused issue in pattern recognition over the past decades. In this paper, we have proposed a new scheme for face recognition using 3D information. In this scheme, the scattered 3D point cloud is first represented with a regular mesh using hierarchical mesh fitting. Then the local shape variation information is extracted to characterize the individual together with the global geometric features. Experimental results on 3D/spl I.bar/RMA, a likely largest 3D face database available currently, demonstrate that the local shape variation information is very important to improve the recognition accuracy and that the proposed algorithm has promising performance with a low computational cost.