nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Detect Faces with Snow

A novel learning approach for face detection in still images using a network of linear units is presented. The SNoW learning architecture is a sparse network of linear functions over a pre-defined or incrementally learned feature space and is specifically tailored for learning in the presence of a very large number of features. A wide range of face images in different poses, with different expressions and under different lighting conditions are used as a training set to capture the variations of human faces. Experimental results on commonly used CMU data sets show that the SNoW-based approach perform well against methods that use neural networksneural network, Bayesian classifiers, Support Vector Machines and others. To quantify and explain the experimental results, we present a theoretical analysis that shows the advantage of this architecture and traces it to the nature of the update rule used in SNoW, a multiplicative update rule based on the Winnow learning algorithm. In particular, in sparse domains (in which the number of irrelevant features is large) this update rule is shown to be advantageous relative to algorithms that are derived from additive update rules such as Perceptron and Support Vector Machines. We show that learning problems in the visual domain have these sparseness characteristics and exhibit it by analyzing data taken from face detection experiments. Our experiments exhibit good generalization and robustness properties of the SNoW-based method, and conform to the theoretical analysis.
Face Recognition Using Kernel Methods

Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition, and tracking. The representation in these subspace methods is based on second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics and Independent Component Analysis (ICA) have been used as informative low dimensional representations for visual recognition. In this paper, we investigate the use of Kernel Principal Component Analysis and Kernel Fisher Linear Discriminant for learning low dimensional representations for face recognition, which we call Kernel Eigenface and Kernel Fisherface methods. While Eigenface and Fisherface methods aim to find projection directions based on the second order correlation of samples, Kernel Eigenface and Kernel Fisherface methods provide generalizations which take higher order correlations into account. We compare the performance of kernel methods with Eigenface, Fisherface and ICA-based methods for face recognition with variation in pose, scale, lighting and expression. Experimental results show that kernel methods provide better representations and achieve lower error rates for face recognition.
Face Detection and Gesture Recognition for Human-Computer Interaction

With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture. Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition. Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.
Skin Color Model

Human skin color has been used and proved to be an effective feature in many applications from human face detection to hand tracking. However, most studies use either simple thresholding or a single Gaussian distribution to characterize the properties of skin color. Although skin colors of different races fall into a small cluster in normalized RGB or HSV color space, we find that a single Gaussian distribution is neither sufficient to model human skin color nor effective in general applications. Further, previous approaches use small collections of images to estimate the density function but do not validate the models by verifying the statistical fit of the chosen model to the data. The work in this chapter is aimed at estimating the properties of human skin color using the Michigan face database (http://www.engin.umich.edu/faces/) which consists of 2,447 images of human faces from different ethnic groups. More than 9.5 million skin color pixels are used to build a skin color model.
Face detection using mixtures of linear subspaces

We present two methods using mixtures of linear sub-spaces for face detection in gray level images. One method uses a mixture of factor analyzers to concurrently perform clustering and, within each cluster, perform local dimensionality reduction. The parameters of the mixture model are estimated using an EM algorithm. A face is detected if the probability of an input sample is above a predefined threshold. The other mixture of subspaces method uses Kohonen's self-organizing map for clustering and Fisher linear discriminant to find the optimal projection for pattern classification, and a Gaussian distribution to model the class-conditioned density function of the projected samples for each class. The parameters of the class-conditioned density functions are maximum likelihood estimates and the decision rule is also based on maximum likelihood. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used as the training set to capture the variations of human faces. Our methods have been tested on three sets of 225 images which contain 871 faces. Experimental results on the first two datasets show that our methods perform as well as the best methods in the literature, yet have fewer false detects.
Face recognition using kernel eigenfaces

Eigenface or principal component analysis (PCA) methods have demonstrated their success in face recognition, detection, and tracking. The representation in PCA is based on the second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Higher order statistics (HOS) have been used as a more informative low dimensional representation than PCA for face and vehicle detection. We investigate a generalization of PCA, kernel principal component analysis (kernel PCA), for learning low dimensional representations in the context of face recognition. In contrast to HOS, kernel PCA computes the higher order statistics without the combinatorial explosion of time and memory complexity. While PCA aims to find a second order correlation of patterns, kernel PCA provides a replacement which takes into account higher order correlations. We compare the recognition results using kernel methods with eigenface methods on two benchmarks. Empirical results show that kernel PCA outperforms the eigenface method in face recognition.
A geometric approach to train support vector machines

Support Vector Machines (SVMs) have shown great potential in numerous visual learning and pattern recognition problems. The optimal decision surface of a SVM is constructed from its support vectors which are conventionally determined by solving a quadratic programming (QP) problem. However, solving a large optimization problem is challenging since it is computationally intensive and the memory requirement grows with square of the training vectors. In this paper, we propose a geometric method to extract a small superset of support vectors, which we call guard vectors, to construct the optimal decision surface. Specifically, the guard vectors are found by solving a set of linear programming problems. Experimental results on synthetic and real data sets show that the proposed method is more efficient than conventional methods using QPs and requires much less memory.
Sex with Support Vector Machines

Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21- by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature.
Gender classification with support vector machines

Support vector machines (SVM) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1755 images from the FERET face database. The performance of SVM (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques such as radial basis function (RBF) classifiers and large ensemble-RBF networks. SVM also out-performed human test subjects at the same task: in a perception study with 30 human test subjects, ranging in age from mid-20s to mid-40s, the average error rate was found to be 32% for the "thumbnails" and 6.7% with higher resolution images. The difference in performance between low- and high-resolution tests with SVM was only 1%, demonstrating robustness and relative scale invariance for visual classification.
Support vector machines for visual gender classification

Support vector machines (SVM) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1755 images from the FERET face database. The performance of SVM (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques such as radial basis function (RBF) classifiers and large ensemble-RBF networks. Surprisingly, SVM also out-performed human test subjects at the same task: in an experimental study involving 30 human test subjects ranging in age from mid-20s to mid-40s, the average error rate was 32% for the same "thumbnails" and 6.7% with high-resolution images (still nearly twice the error rate of SVM). The difference between low and high-resolution inputs with SVM was only 1% thus demonstrating a degree of robustness and relative scale invariance.
Ergosteryl Myristate, a New Ergosterol Derivative from Unidentified Marine Algicolous Fungus

A new ergosteryl myristate (1) and ergosterol (2) have been isolated from the organic extract of the mycelium of unidentified marine algicolous fungus, isolate MF001. The structure of a new compound was assigned on the basis of comprehensive spectroscopic analyses and chemical synthesis.
Face detection using a mixture of factor analyzers

We present a probabilistic method to detect human faces using a mixture of factor analyzers. One characteristic of this mixture model is that it concurrently performs clustering and, within each cluster, local dimensionality reduction. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used as the training set to capture the variations of human faces. In order to fit the mixture model to the sample face images, the parameters are estimated using an EM algorithm. Experimental results show that faces in different poses, with different facial expressions, and under different lighting conditions are accurately detected by our method.
Recognizing hand gesture using motion trajectories

We present an algorithm for extracting and classifying two-dimensional motion in an image sequence based on motion trajectories. First, a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain 2-view correspondences. Affine transformations are computed from each pair of corresponding regions to define pixel matches. Pixels matches over consecutive images pairs are concatenated to obtain pixel-level motion trajectories across the image sequence. Motion patterns are learned from the extracted trajectories using a time-delay neural network. We apply the proposed method to recognize 40 hand gestures of American Sign Language. Experimental results show that motion patterns in hand gestures can be extracted and recognized with high recognition rate using motion trajectories.
A data partition method for parallel self-organizing map

We propose a method to partition training vectors into clusters for a parallel implementation of self-organizing map (SOM) algorithm. The proposed algorithm assigns a cluster to a processor such that, in updating weights, the neighbourhoods of a winning node in a cluster do not overlap the neighboring nodes of some winning nodes in other clusters. It reduces the overheads caused by synchronization (i.e., maintaining coherency) of the weight matrices in the processors since the proposed algorithm allows multiple vectors to find their winning nodes and update weights in parallel. Our experimental results show that an average speedup of 3.15 for a parallel implementation of a four processor simulation.
Extraction and classification of visual motion patterns for hand gesture recognition

We present a new method for extracting and classifying motion patterns to recognize hand gestures. First, motion segmentation of the image sequence is generated based on a multiscale transform and attributed graph matching of regions across frames. This produces region correspondences and their affine transformations. Second, color information of motion regions is used to determine skin regions. Third, human head and palm regions are identified based on the shape and size of skin areas in motion. Finally, affine transformations defining a region's motion between successive frames are concatenated to construct the region's motion trajectory. Gestural motion trajectories are then classified by a time-delay neural network trained with backpropagation learning algorithm. Our experimental results show that hand gestures can be recognized well using motion patterns.
Extracting gestural motion trajectories

This paper is concerned with the extraction of spatio-temporal patterns in video sequences with focus on trajectories of gestural motions associated with American Sign Language. An algorithm is described to extract the motion trajectories of salient features such as human palms from an image sequence. First, motion segmentation of the image sequence is generated based on a multiscale segmentation of the frames and attributed graph matching of regions across frames. This produces region correspondences and their affine transformations. Second, colors of the moving regions are used to determine skin regions. Third, the head and palm regions are identified based on the shape and size of skin regions in motion. Finally, affine transformations defining a region's motion between successive frames are concatenated to construct the region's motion trajectory. Experimental results showing the extracted motion trajectories are presented.
Gaussian mixture model for human skin color and its applications in image and video databases

This paper is concerned with estimating a probability density function of human skin color, using a finite Gaussian mixture model, whose parameters are estimated through the EM algorithm. Hawkins' statistical test on the normality and homoscedasticity (common covariance matrix) of the estimated Gaussian mixture models is performed and McLachlan's bootstrap method is used to test the number of components in a mixture. Experimental results show that the estimated Gaussian mixture model fits skin images from a large database. Applications of the estimated density function in image and video databases are presented.
Recognizing machining features through partial constraint satisfaction

Machining features are groupings of geometric and topological entities that have certain engineering significance. Recognizing these features from a designed parts is crucial in the automation of computer integrated manufacturing (CIM). In this paper, the machining feature recognition problem is formulated as a partial constraint satisfaction problem (PCSP) where variables are the faces in the delta volume (the difference between a part and a stock), the possible feature classifications are values for the variables, and the constraints are the geometric and topological properties between the variables. Based on this framework, several techniques can be applied to solve the PCSP. An integral method that combines extended forward checking, variable ordering and value ordering is employed to solve the problem. Interacting features are then recognized by solving the PCSP and verifying the solution that partially satisfy all the constraints.
Robotic Mapping with Polygonal Random Fields

Two types of probabilistic maps are popular in the mobile robotics literature: occupancy grids and geometric maps. Occupancy grids have the advantages of simplicity and speed, but they represent only a restricted class of maps and they make incorrect independence assumptions. On the other hand, current geometric approaches, which characterize the environment by features such as line segments, can represent complex environments compactly. However, they do not reason explicitly about occupancy, a necessity for motion planning; and, they lack a complete probability model over environmental structures. In this paper we present a probabilistic mapping technique based on polygonal random fields (PRF), which combines the advantages of both approaches. Our approach explicitly represents occupancy using a geometric representation, and it is based upon a consistent probability distribution over environments which avoids the incorrect independence assumptions made by occupancy grids. We show how sampling techniques for PRFs can be applied to localized laser and sonar data, and we demonstrate significant improvements in mapping performance over occupancy grids.
A robust architecture for distributed inference in sensor networks

Many inference problems that arise in sensor networks require the computation of a global conclusion that is consistent with local information known to each node. A large class of these problems-including probabilistic inference, regression, and control problems-can be solved by message passing on a data structure called a junction tree. In this paper, we present a distributed architecture for solving these problems that is robust to unreliable communication and node failures. In this architecture, the nodes of the sensor network assemble themselves into a junction tree and exchange messages between neighbors to solve the inference problem efficiently and exactly. A key part of the architecture is an efficient distributed algorithm for optimizing the choice of junction tree to minimize the communication and computation required by inference. We present experimental results from a prototype implementation on a 97-node Mica2 mote network, as well as simulation results for three applications: distributed sensor calibration, optimal control, and sensor field modeling. These experiments demonstrate that our distributed architecture can solve many important inference problems exactly, efficiently, and robustly.
Exploiting locality in probabilistic inference

This thesis investigates computational properties of decomposable probability models, which can be represented in terms of marginals over subsets of variables. We show that decomposable representations have significant locality structure that can be exploited to yield effective solutions to two important problems. 
The first problem is filtering in dynamic Bayesian networks, which is typically intractable because the belief state collapses to a representation with no independence structure. We present a novel technique for filtering, called thin junction tree filtering (TJTF), that approximates the belief state by a decomposable model. By exploiting locality in the belief state representation, TJTF can automatically and efficiently identify the weakest dependencies in the belief state and prune them to control the complexity of filtering. We apply TJTF to simultaneous localization and mapping, a fundamental problem in mobile robotics, and obtain a solution that performs comparably with exact methods but with substantially better time and space complexity. 
The second problem is distributed inference, where the nodes of a network collaborate to solve an inference problem. We present a robust architecture for distributed inference in which the nodes assemble themselves into a junction tree and solve the inference problem by message passing. In settings with unreliable communication and node failures, traditional message passing algorithms can fail because nodes cannot access the complete probability model. We present a new message passing algorithm that exploits the locality of decomposable representations to guarantee that each node can make inferences using whatever parts of the model are available. The algorithm is exact at convergence, and when parts of the model are inaccessible, its estimate is an informative approximation to the true posterior. The approach is demonstrated on the problem of automatic sensor calibration in wireless sensor networks.
Distributed regression: an efficient framework for modeling sensor network data

We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing the communication required. After the algorithm is run, each node can answer queries for its local region, or the nodes can efficiently transmit the parameters of the model to a user outside the network. We present an evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab, demonstrating that our distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.