nils-herrmann/clean_scirepeval huggingface dataset:
Parameter Estimation for Optimal Object Recognition: Theory and Application

Object recognition systems involve parameters such as thresholds, bounds and weights. These parameters have to be tuned before the system can perform successfully. A common practice is to choose such parameters manually on an ad hoc basis, which is a disadvantage. This paper presents a novel theory of parameter estimation for optimization-based object recognition where the optimal solution is defined as the global minimum of an energy function. The theory is based on supervised learning from examples. Correctness and instability are established as criteria for evaluating the estimated parameters. A correct estimate enables the labeling implied in each exemplary configuration to be encoded in a unique global energy minimum. The instability is the ease with which the minimum is replaced by a non-exemplary configuration after a perturbation. The optimal estimate minimizes the instability. Algorithms are presented for computing correct and minimal-instability estimates. The theory is applied to the parameter estimation for MRF-based recognition and promising results are obtained.
Multiple layer based background maintenance in complex environment

A fast and efficient multiple layer background maintenance model is built to conserve the original and the current background separately. Fusing the properties of object motion in image pixels and the changes between the input video and the multiple background layers, this method could handle various sources of scene changes, including ghosts, abandon objects and illumination changes. An intelligent video surveillance system is developed to test the performance of the algorithm. 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 maintenance in complex environment.
Shape localization based on statistical method using extended local binary pattern

Accurate localization of representative points of a face is crucial to many face analysis and synthesis problems. Active shape model (ASM) is a powerful statistical tool for face alignment. However, it suffers from variations of pose, illumination and expressions. In this paper, we analyze the mechanism of active shape model and realize that the ability of normal profiles to describe the local appearance pattern is very limited. For efficient appearance pattern representation, the local binary pattern is used and extended to describe the local patterns of facial key points. For the purpose of retaining the spatial information, sub-images of key points are divided into several regions, which are combined to define the extended local binary pattern (ELBP) histogram. Then we propose an improved ASM method framework, ELBP-ASM, in which local appearance patterns of key points are modelled using extended local binary pattern. Experimental results demonstrate that ELBP-ASM achieves more accurate results compared with original method used in ASM.
Face Recognition: Technical Challenges and Research Directions

Face recognition performance has improved significantly since the first automatic face recognition system developed by Kanade Face detection, facial feature extraction, and recognition can now be performed in “realtime” for images captured under favorable, constrained situations Although progress in face recognition has been encouraging, the task has also turned out to be a difficult endeavor, especially for unconstrained tasks where viewpoint, illumination, expression, occlusion, accessories, and so on vary considerably In this talk, I will analyze challenges from the viewpoint of face manifolds and points out possible research directions towards highly accurate face recognition I will show that the challenges come from high nonconvexity of face manifolds, in the image space, under variations in lighting, pose and so on; unfortunately, there have been no good methods from theories of pattern recognition for solving such difficult problems, especially when the size of training data is small However, there are two directions to look at towards possible solutions: One is to construct a “good” feature space in which the face manifolds become less complex i.e., less nonlinear and nonconvex than those in other spaces This includes two levels of processing: (1) normalize face images geometrically and photometrically, such as using morphing and histogram equalization; and (2) extract features in the normalized images which are stable with respect to the said variations, such as based on Gabor wavelets The second strategy is to construct classification engines able to solve less, although still, nonlinear problems in the feature space, and to generalize better A successful algorithm usually combines both strategies Still another direction is on system design, including sensor hardware, to make the pattern recognition problems thereafter less challenging.
FloatBoost learning and statistical face detection

A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.
Evaluation of face alignment solutions using statistical learning

We propose a statistical learning approach for constructing an evaluation function for face alignment. A nonlinear classification function is learned from a set of positive (good alignment) and negative (bad alignment) training examples to effectively distinguish between qualified and un-qualified alignment results. The AdaBoost learning algorithm is used, where weak classifiers are constructed based on edge features and combined into a strong classifier. Several strong classifiers are learned in stages using bootstrap samples during the training. The evaluation function thus learned gives a quantitative confidence and the good-bad classification is achieved by comparing the confidence with a learned optimal threshold. We point out the importance of using cascade strategy in the stagewise learning of strong classifiers. The divide-and-conquer strategy not only dramatically increases the speed of classification, but also makes the training easier and the good-bad classification more effective. Experimental results demonstrate that the classification function learned using the proposed approach provides semantically more meaningful scoring than the reconstruction error used in AAM for classification between qualified and un-qualified face alignment.
Face alignment under variable illumination

This paper presents an approach to face alignment under variable illumination, an obstacle largely ignored in previous 2D alignment work. To account for illumination variation, our method employs two forms of relatively lighting-invariant information. Edge phase congruency is adopted to coarsely localize facial features, since prominent feature edges can be robustly located in the presence of shading and shadows. To accurately deal with features with less pronounced edges, final alignment is then computed from intrinsic gray-level information recovered using a proposed form of local intensity normalization. With this approach, our face alignment system works efficiently and effectively under a wide range of illumination conditions, as evidenced by extensive experimentation.
Nearest manifold approach for face recognition

Faces under varying illumination, pose and non-rigid deformation are empirically thought of as a highly nonlinear manifold in the observation space. How to discover intrinsic low-dimensional manifold is important to characterize meaningful face distributions and classify them using a simpler, such as linear or Gaussian based, classifier. In this paper, we present a manifold learning algorithm (MLA) for learning a mapping from highly-dimensional manifold into the intrinsic low-dimensional linear manifold. We also propose the nearest manifold (NM) criterion for the classification and present an algorithm for computing the distance from the sample to be classified to the nearest face manifolds in light of local linearity of manifold. Based on these works, face recognition is achieved with the combination of MLA and NM. Experiments on several face databases show that the advantages of our proposed combinational approach.
Robust Estimation of Rotation Angles from Image Sequences Using the Annealing M-Estimator

A robust method is presented for computing rotation angles of image sequences from a set of corresponding points containing outliers. Assuming known rotation axis, a least-squares (LS) solution are derived to compute the rotation angle from a clean data set of point correspondences. Since clean data is not guaranteed, we introduce a robust solution, based on the M-estimator, to deal with outliers. Then we present an enhanced robust algorithm, called the annealing M-estimator (AM-estimator), for reliable robust estimation. The AM-estimator has several attractive advantages over the traditional M-estimator: By definition, the AM-estimator involves neither scale estimator nor free parameters and hence avoids instabilities therein. Algorithmically, it uses a deterministic annealing technique to approximate the global solution regardless of the initialization. Experimental results are presented to compare the performance of the LS, M- and AM-estimators for the angle estimation. Experiments show that in the presence of outliers, the M-estimator outperforms the LS estimator and the AM-estimator outperforms the M-estimator.
Face recognition under varying lighting conditions using self quotient image

In this paper, we introduce the concept of self-quotient image (SQI) for robust face recognition under varying lighting conditions. It is based on the quotient image method (Amnon Shashua et al., 2001) (T. Riklin-Raviv et al., 1999) to achieve lighting invariance. However, the SQI has three advantages: (1) it needs only one face image for extraction of intrinsic lighting invariant property of a face while removing extrinsic factor corresponding to the lighting; (2) no alignment is needed; (3) it works in shadow regions. The theoretical analysis on conditions where the algorithm is applicable and a non-iterative filtering algorithm for computing SQI are presented. Experiment results demonstrate the effectiveness of our method for robust face recognition under varying lighting conditions.
Face alignment using statistical models and wavelet features

Active shape model (ASM) is a powerful statistical tool for face alignment by shape. However, it can suffer from changes in illumination and facial expression changes, and local minima in optimization. In this paper, we present a method, W-ASM, in which Gabor wavelet features are used for modeling local image structure. The magnitude and phase of Gabor features contain rich information about the local structural features of face images to be aligned, and provide accurate guidance for search. To a large extent, this repairs defects in gray scale based search. An E-M algorithm is used to model the Gabor feature distribution, and a coarse-to-fine grained search is used to position local features in the image. Experimental results demonstrate the ability of W-ASM to accurately align and locate facial features.
Face alignment using view‐based direct appearance models

Accurate face alignment is the prerequisite for many computer vision problems, such as face recognition, synthesis and 3D face modeling. In this article, a novel appearance model, called direct appearance model (DAM), is proposed and its extended view‐based models are applied for multiview face alignment. Similar to the active appearance model (AAM), DAM also makes ingenious use of both shape and texture constraints; however, it does not combine them as in AAM; texture information is used directly to predict the shape and estimate the position and appearance (hence the name DAM). The way that DAM models shapes and textures has the following advantages as compared with AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) it can converge more quickly and has higher accuracy, and (3) the memory requirement is cut down to a large extent. Extensive experiments are presented to evaluate the DAM alignment in comparison with AAM.
Multi-modal face tracking using Bayesian network

We present a Bayesian network based multimodal fusion method for robust and real-time face tracking. The Bayesian network integrates a prior of second order system dynamics, and the likelihood cues from color, edge and face appearance. While different modalities have different confidence scales, we encode the environmental factors related to the confidences of modalities into the Bayesian network, and develop a Fisher discriminant analysis method for learning optimal fusion. The face tracker may track multiple faces under different poses. It is made up of two stages. First hypotheses are efficiently generated using a coarse-to-fine strategy; then multiple modalities are integrated in the Bayesian network to evaluate the posterior of each hypothesis. The hypothesis that maximizes a posterior (MAP) is selected as the estimate of the object state. Experimental results demonstrate the robustness and real-time performance of our face tracking approach.
Content-based audio classification and retrieval by support vector machines

Support vector machines (SVMs) have been recently proposed as a new learning algorithm for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the audio classification problem. We illustrate the potential of SVMs on a common audio database, which consists of 409 sounds of 16 classes. We compare the SVMs based classification with other popular approaches. For audio retrieval, we propose a new metric, called distance-from-boundary (DFB). When a query audio is given, the system first finds a boundary inside which the query pattern is located. Then, all the audio patterns in the database are sorted by their distances to this boundary. All boundaries are learned by the SVMs and stored together with the audio database. Experimental comparisons for audio retrieval are presented to show the superiority of this novel metric to other similarity measures.
PARAMETER OPTIMIZATION FOR ACTIVE SHAPE MODELS

Active Shape Models (ASM) is a powerful statistical tool for extracting objects, e.g. face, from images. It is composed of two parts: ASM model and ASM search. In ASM, these two parts are treated separately. First, ASM model is trained. Then, ASM search is performed using this model. However, we find that these two parts are closely interrelated. The performance of ASM depends on both of them. Improvement on one of them does not consequentially improve the overall performance, for it may worsen the other. In this paper, we find the key parameter that relates these two parts: subspace explanation proportion. By optimizing subspace explanation proportion, the overall performance of ASM can improve by a percentage of about 20 in our experiments. Furthermore, this paper proposes to decompose the ASM overall error into ASM model subspace reconstruction error and ASM search error, proving that the square of the subspace reconstruction error is linearly related with the subspace explanation proportion and finding that the square of the search error is a piecewise function of the explanation proportion. This decomposition is a new method for further analysis and possible improvement. Based on this decomposition, we propose a method to estimate the optimal explanation proportion. Experiments show that the estimation is satisfactory.
Content-based audio classification and segmentation by using support vector machines

Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.
Illumination modeling and normalization for face recognition

We present a general framework for face modeling under varying lighting conditions. First, we show that a face lighting subspace can be constructed based on three or more training face images illuminated by noncoplanar lights. The lighting of any face image can be represented as a point in this subspace. Second, we show that the extreme rays, i.e. the boundary of an illumination cone, cover the entire light sphere. Therefore, a relatively sparsely sampled face images can be used to build a face model instead of calculating each extremely illuminated face image. Third, we present a face normalization algorithm, illumination alignment, i.e. changing the lighting of one face image to that of another face image. Experiments are presented.
Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning

Research has been devoted in the past few years to relevance feedback as an effective solution to improve performance of content-based image retrieval (CBIR). In this paper, we propose a new feedback approach with progressive learning capability combined with a novel method for the feature subspace extraction. The proposed approach is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. Positive examples are used to estimate a Gaussian distribution that represents the desired images for a given query; while the negative examples are used to modify the ranking of the retrieved candidates. In addition, feature subspace is extracted and updated during the feedback process using a principal component analysis (PCA) technique and based on user's feedback. That is, in addition to reducing the dimensionality of feature spaces, a proper subspace for each type of features is obtained in the feedback process to further improve the retrieval accuracy. Experiments demonstrate that the proposed method increases the retrieval speed, reduces the required memory and improves the retrieval accuracy significantly.
Learning to boost GMM based speaker verification

The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing weights, we believe that there exists a better means of combining individual Gaussian mixture models. The proposed AdaBoost-GMM method is non-parametric in which a selected set of weak classifiers, each constructed based on a single Gaussian model, is optimally combined to form a strong classifier, the optimality being in the sense of maximum margin. Experiments show that the boosted GMM classifier yields 10.81% relative reduction in equal error rate for the same handsets and 11.24% for different handsets, a significant improvement over the baseline adapted GMM system.
Real-Time Face Detection Using Boosting Learning 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, without affecting computational complexity. We show, based on statistics of face and non-face examples, that weak classifiers learned in hierarchical feature spaces are better boosted. Our methodology leads to a face detection system that achieves higher performance than the current state-of-the-art system, at a comparable speed.
FPGA-based SIMD processor

A massively parallel single instruction multiple data stream (SIMD) processor designed specifically for cryptographic key search applications is presented. This design exploits fine grain parallelism and the high memory bandwidth available in an FPGA (field programmable gate array) by integrating 95 simple processors and memory on a single FPGA chip. Performance is compared with a previously reported hardwired design on a RC4 key search application.
Multi-view face alignment using direct appearance models

Alignment makes face distribution statistically more compact than un-aligned faces and provides a good basis for face modeling, recognition and synthesis. In this paper we present a method for multi-view face alignment using a new model called direct appearance model (DAM). Like active appearance model (AAM), DAM also makes use of both shape and texture constraints; however it does this without combining shape and texture as in AAM. The way that DAM models shapes and textures has the following advantages as compared to AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) the convergence and accuracy are improved, and (3) the memory requirement is cut down to a large extent. Extensive experiments are presented to evaluate the DAM alignment in comparison with AAM.
Multi-view face detection with FloatBoost

In this paper, a new boosting algorithm, called FloatBoost, is proposed to construct a strong face-nonface classifier. FloatBoost incorporates the idea of Floating Search into AdaBoost, and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. We also present a novel framework for fast multi-view face detection. A detector-pyramid architecture is designed to quickly discard a vast number of non-face sub-windows and hence perform multi-view face detection efficiently. This results in the first real-time multi-view face detection system which runs at 5 frames per second for 320x240 image sequence.