nils-herrmann/clean_scirepeval huggingface dataset:
AuthenMetric F 1 : A Highly Accurate and Fast Face Recognition System

AuthenMetric F1 is a face-based biometric authentication system.  One (F1), the system is fast and accurate. Better than that F1, this F1 makes no disasters, hence can save money and human lives. As a demo at ICCV, it must have been made of scientific ingredients of computer vision, such as innovative imaging hardware, local feature extraction and statistical learning; it is further enhanced by engineering inspirations. The system is high in both speed and accuracy. A users can be identified quickly once he/she sees him/herself in a mirror or LCD, at a distance of an arm away from the camera. The performance is regardless of illumination changes. Interested? Come to user-experience the magic. Technically, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The hardware minimizes environmental lighting and delivers face images with frontal lighting. This avoids many problems in subsequent face processing to a great extent. The face detection and recognition algorithms are based on a local feature representation. Statistical learning is applied to learn most effective features and classifiers for building face detection and recognition engines. The novel imaging system and the detection and recognition engines are integrated into a powerful face recognition system. Evaluated in real-world user scenario, a condition that is harder than a technology evaluation such as Face Recognition Vendor Tests (FRVT), the system has demonstrated excellent accuracy, speed and usability.
Null space-based kernel Fisher discriminant analysis for face recognition

The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Furthermore, kernel technique is incorporated into our null space method. Firstly, all samples are mapped to the kernel space through an efficient kernel function, called cosine kernel, which have been demonstrated to increase the discriminating capability of the original polynomial kernel function. Secondly, a truncated NLDA is employed. The novel approachh only requires one eigenvalu analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed method.
SHAPE EVALUATION FOR WEIGHTED ACTIVE SHAPE MODELS

Active Shape Models (ASM) is a powerful statistical tool for face alignment. However, no evaluation is performed on the final results. Nevertheless, the shape evaluation information is very useful for the search and the final results. In this paper, a shape evaluation method and a new search algorithm, called weighted ASM, are proposed. The shape evaluation is based on the local appearance model of ASM to determine how well the searching shape match models derived from the training set. It is used to guide the search procedure to get more accurate results. The weighted-ASM also uses this evaluation information to project the searching shape into the solution shape space in a weighted way. Compared with ASM’s orthogonal projection, the weighted projection can drag the search out of local minima to be more accurate and more robust. Experiments have been done to show the ability of this method to align shapes.
Nearest intra-class space classifier for face recognition

We propose a novel classification method, called nearest intra-class space (NICS), for face recognition. In our method, the distribution of face patterns of each person is represented by the intra-class space to capture all intra-class variations. Then, a regular principal subspace is derived from each intra-class space using principal component analysis. The classification is based on the nearest weighted distance, combining distance-from-subspace and distance-in-subspace, between the query face and each intra-class subspace. Experimental results show that the NICS classifier outperforms other classifiers in terms of recognition performance.
Boosting Local Feature Based Classifiers for Face Recognition

In this paper, we present a method for face recognition using boosted Gabor feature based classifiers. Weak classifiers are constructed based on both magnitude and phase features derived from Gabor filters [Quadrature-phase simple-cell pairs are ap-propriately described in complex analytic from]. The multi-class problem is transformed into a two-class one of intra- and extra-class classification using intra-personal and extra-personal difference images, as in [Beyond euclidean eigenspaces:bayesian matching for visian recognition]. A cascade of strong classifiers are learned using bootstrapped negative examples, similar to the way in face detection framework [Robust real time object detection]. The combination of classifiers based on two different types of features produces better results than using either type. Experiments on FERET database show good results comparable to the best one reported in literature [The FERET evaluation methodology for face-recognition algorithms].
Generalized quotient image

We present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI, spherical harmonic, image ratio and retinex. Under this framework, we generalize these previous works into two new algorithms: (1) non-point light quotient image (NPL-QI) extends QI to deal with non-point light sources by modeling non-point light directions using spherical harmonic bases; (2) self-quotient image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.
Statistical Learning of Evaluation Function for ASM/AAM Image Alignment

Alignment between the input and target objects has great impact on the performance of image analysis and recognition system, such as those for medical image and face recognition. Active Shape Models (ASM)  and Active Appearance Models (AAM) [2, 3] provide an important framework for this task. However, an effective method for the evaluation of ASM/AAM alignment results has been lacking. Without an alignment quality evaluation mechanism, a bad alignment cannot be identified and this can drop system performance.
Optimal Shape Space and Searching in ASM Based Face Alignment

The Active Shape Models (ASM) is composed of two parts: the ASM shape model and the ASM search The standard ASM, with the shape variance components all discarded and searching in image subspace and shape subspace independently, has blind searching and unstable search result In this paper, we propose a novel idea, called Optimal Shape Subspace, for optimizing ASM search It is constructed by both main shape and shape variance information It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence is more expressive in representing shapes in real life A cost function is developed, based on a careful study on the search process especially regarding relations between the ASM shape model and the ASM search An Optimal Searching method using the feedback provided by the evaluation cost can significantly improve the performance of ASM alignment This is demonstrated by experimental results.
Boosting Local Binary Pattern (LBP)-Based Face Recognition

This paper presents a novel approach for face recognition by boosting statistical local features based classifiers The face image is scanned with a scalable sub-window from which the Local Binary Pattern (LBP) histograms  are obtained to describe the local features of a face image The multi-class problem of face recognition is transformed into a two-class one by classifying every two face images as intra-personal or extra-personal ones [9] The Chi square distance between corresponding Local Binary Pattern histograms of two face images is used as discriminative feature for intra/extra-personal classification We use AdaBoost algorithm to learn a similarity of every face image pairs The proposed method was tested on the FERET FA/FB image sets and yielded an exciting recognition rate of 97.9%.
Learning with Cascade for Classification of Non-Convex Manifolds

Images of a visual object, such as human face, reside in a complicated manifold in the high dimensional image space, when the object is subject to variations in pose, illumination, and other factors. Viola and Jones have successfully tackled difficult nonlinear classification problem for face detection using AdaBoost learning. Moreover, their simple-to-complex cascade of classifiers structure makes the learning and classification even more effective. While training with cascade has been used effectively in many works [4, 5, 6, 7, 2, 3, 8, 9, 10], an understanding of the role of the cascade strategy is still lacking. In this paper, we analyze the problem of classifying non-convex manifolds using AdaBoost learning with and without using cascade. We explain that the divide-and-conquer strategy in cascade learning has a great contribution on learning a complex classifier for non-convex manifolds. We prove that AdaBoost learning with cascade is effective when a complete or over-complete set of features (or weak classifiers) is available. Experiments with both synthesized and real data demonstrate that AdaBoost learning with cascade leads to improved convergence and accuracy.
Subspace analysis and optimization for AAM based face alignment

Active appearance models (AAM) is very powerful for extracting objects, e.g. faces, from images. It is composed of two parts: the AAM subspace model and the AAM search. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. In this paper, an approach is proposed to optimize the subspace model while considering the search procedure. We first perform a subspace error analysis, and then to minimize the AAM error we propose an approach which optimizes the subspace model according to the search procedure. For the subspace error analysis, we decomposed the subspace error into two parts, which are introduced by the subspace model and the search procedure respectively. This decomposition shows that the optimal results of AAM can be achieved only by optimizing both of them jointly rather than separately. Furthermore, based on this error decomposition, we develop a method to find the optimal subspace model according to the search procedure by considering both the two decomposed errors. Experimental results demonstrate that our method can find the optimal AAM subspace model rapidly and improve the performance of AAM significantly.
Null Space Approach of Fisher Discriminant Analysis for Face Recognition

The null space of the within-class scatter matrix is found to express most discriminative information for the small sample size problem (SSSP). The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Furthermore, kernel technique is incorporated into discriminant analysis in the null space. Firstly, all samples are mapped to the kernel space through a better kernel function, called Cosine kernel, which is proposed to increase the discriminating capability of the original polynomial kernel function. Secondly, a truncated NLDA is employed. The novel approach only requires one eigenvalue analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed methods.
Color based grayscale-fused image enhancement algorithm for video surveillance

Multiple sensors image fusion is increasingly being employed in video surveillance. This paper presents a color based grayscale-fused image enhancement algorithm. The main advantage of this algorithm is to stand out the meaningful information of a certain sensor in the selected band of the color space, while maintaining the high resolution of the grayscale-fused image. Moreover, a performance evaluation method based on analyzing the average gradients of the color-fused image is presented and experiments are performed using images under complex environment. The results show that the addition of color to grayscale-fused image can significantly increase observer's sensitivity and the accuracy of further classification and recognition.
Minimization of MRF Energy with Relaxation Labeling

Recently, there has been increasing interest in Markovrandom field (MRF) modeling for solving a variety of computer visionproblems formulated in terms of the maximum a posteriori(MAP) probability. When the label set is discrete, such as in imagesegmentation and matching, the minimization is combinatorial. Theobjective of this paper is twofold: Firstly, we propose to use thecontinuous relaxation labeling (RL) as an alternative approach forthe minimization. The motivation is that it provides a goodcompromise between the solution quality and the computational cost.We show how the original combinatorial optimization can be convertedinto a form suitable for continuous RL. Secondly, we compare variousminimization algorithms, namely, the RL algorithms proposed byRosenfeld et al., and by Hummel and Zucker, the mean field annealing ofPeterson and Soderberg, simulated annealing of Kirkpatrick, theiterative conditional modes (ICM) of Besag and an annealing versionof ICM proposed in this paper. The comparisons are in terms of theminimized energy value (i.e., the solution quality), the requirednumber of iterations (i.e., the computational cost), and also thedependence of each algorithm on heuristics.
Face recognition using Ada-Boosted Gabor features

Face representation based on Gabor features has attracted much attention and achieved great success in face recognition area for the advantages of the Gabor features. However, Gabor features currently adopted by most systems are redundant and too high dimensional. In this paper, we propose a face recognition method using AdaBoosted Gabor features, which are not only low dimensional but also discriminant. The main contribution of the paper lies in two points: (1) AdaBoost is successfully applied to face recognition by introducing the intra-face and extra-face difference space in the Gabor feature space; (2) an appropriate re-sampling scheme is adopted to deal with the imbalance between the amount of the positive samples and that of the negative samples. By using the proposed method, only hundreds of Gabor features are selected. Experiments on FERET database have shown that these hundreds of Gabor features are enough to achieve good performance comparable to that of methods using the complete set of Gabor features.
Real-time face detection using boosting in hierarchical feature spaces

Boosting-based methods have recently led to the state-of-the-art-face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the boosting process, the non-face examples collected by bootstrapping become very similar to the face examples, and the classification error of Haar-like feature based weak classifiers is thus very close to 50%. As a result, the performance of a face detector cannot be further improved. This paper proposed a solution to this problem, introducing a face detection method based on boosting in hierarchical feature spaces (both local and global). We argue that global features, like those derived from principal component analysis, can be advantageously used in the later stages of boosting, when local features do not provide any further benefit. We show that weak classifiers learned in hierarchical feature spaces are better boosted. Our methodology leads to a face detection system that achieves higher performance than a current state-of-the-art system, at a comparable speed.
Self quotient image for face recognition

The reliability of facial recognition techniques is often affected by the variation of illumination, such as shadows and illumination direction changes. In this paper, we present a novel framework, called the self-quotient image, for the elimination of the lighting effect in the image. Although this method has a similar invariant form to the quotient image by Shashua etc. (2001), it does not need the alignment and bootstrap images. Our method combines the image processing technique of edge-preserved filtering with the Retinex applications of by Jobson, et al., (1997) and Gross and Brajovie (2003). We have analyzed this algorithm with a 3D imaging model and formulated the conditions where illumination-invariant and -variant properties can be realized, respectively. A fast anisotropic filter is also presented. The experiment results show that our method is effective in removing the effect of illumination for robust face recognition.
Key geochronology of Mesozoic deformation in the Eastern Block of the North China Craton and its con

The Jiao-Liao massif is an important part of the Eastern Block of the north China Craton and located on the hanging wall 
of the north-dipping Dabie-Sulu suture zone. Several important tectonic models about tectonic evolution of the eastern Asia were 
proposed in the last decade and related closely to the Jiao-Liao massif. This paper combined our SHRIMP U-Pb zircon ages of the 
Dandong Granite in the Southern Liaoning Province, China, with our wide firsthand fieldworks in the east of North China Craton, our 
oilfield works in the Bohai Bay Basin and other recent SHRIMP dating, to summarize Mesozoic tectonic reactivation and evolutionary 
processes of the Eastern Block. In this study we identify an about 160 Ma partial melting of Paleoproterozoic plutons in the Jiao-Liao 
massif. Some magmatic, euhedral single zircons or magmatic zircon rimes around ca. 2100 Ma cores in the Dandong Granites near the 
Liaonan Neoarchaean terrane, are first recognized by the CL and BSE images and identified that the latest partial melting event 
happened at 167 - 157 Ma. This partial melting is most likely derived from in-situ remelting of ancient lower continental crust, mostly 
of North China Craton. The Dandong plutons are aligned in the NE direction and suffered intensively from the subhorizontal ductile 
thrust-related shearing and the subsequent NNE-trending folding. In the Dandong area, the precise timing of deformations was solved in 
this paper. The first deformation is identified to be from 195 to 193 Ma according to the K-Ar and ~(40)Ar/~(39)Ar ages of muscovites in the 
E-W trending shear zones of Liaodong peninsula. Based on the field relationship between the plutons and deformation foliations, a 
range from 153 to 145 Ma is defined as the duration of the second deformation time remained in the Dandong Granites. The NNE strike- 
slip faulting, i. e. the third deformation, must be constrained in a range from 135 Ma to 95 Ma. This deduced result is most similar to 
a range from the ~(40)Ar/~(39)Ar age of 128 - 132 Ma of initial sinistral strike-slipping of the Tan-Lu fault in the Anhui province to the biotite 
 cooling age of 100±2.3 Ma of the Yilan-Yitong segment of the Tanlu fault in the Jilin province. These faults are transtensive to control 
some pull-apart basins. However, during the third deformation, some metamorphic core complexes in the eastern China often took place 
in the overlapping area between the large-scale sinistral faults. It is well correlated to NW-directed subduction of the Paleo-Pacific Plate 
at 180 - 145 Ma. Our SHRIMP data also indicate that the Liaodong basement and Early Mesozoic magmatism are exactly similar to the 
Jiaodong basement and Mesozoic magmatism. Therefore, Early Mesozoic evolution in the Liaodong area, similar to that in the Jiaodong 
area, is also related closely to the Sulu Orogen in the Early Mesozoic and tightly to the Pacific subduction in the entire Mesozoic.
Real-time and accurate segmentation of moving objects in dynamic scene

Fast and accurate segmentation of moving objects in video sequences is a basic task in many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and classification and activity analysis. This paper presents effective methods for solving this problem. Firstly, a fast and efficient algorithm is presented for background update to handle various sources of scene changes, including ghosts, left objects, camera shaking, and abrupt illumination changes. This is done by analyzing properties of object motion in image pixels and temporal frames, and combining both levels of constraints. Moreover, the algorithm does not need training sequence. Secondly, a real-time and accurate moving object segmentation algorithm is presented for moving object localization. Here, a novel filtering method is presented based on multiple scale and fast connected blob extraction. An intelligent video surveillance system is developed to test the performance of the algorithms. Experiments are performed using long video sequences under different conditions indoor and outdoor. The results show that the proposed algorithm is effective and efficient in real-time and accurate background update and moving object segmentation.
Shape and texture preserved non-photorealistic rendering: Research Articles

Two approaches, image-space and object-space, exist for non-photorealistic rendering (NPR). The object-space approach has an advantage that it is able to access the 3D shape information of the scene with a 3D model. In this paper, we present a non-stroke based image-space technique that has the strength similar to that of the object-space approach, but without involving explicit 3D models. The basic idea is to factorise the Lambertian model to obtain the shape and texture information implicitly contained in the image. We also develop a specific anisotropic low-pass image filter, which can smooth an image without blurring important features. With the developed techniques, we present two NPR styles, the sketching style and the painting style. Although our work does not aim to mimic any specific existing art form, the sketching style can generate effects similar to those of the pen-and-ink and the painting style is able to produce the colour permeating effects of watercolour.