nils-herrmann/clean_scirepeval huggingface dataset:
Learning from examples in the small sample case: face expression recognition

Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case.
Age estimation with expression changes using multiple aging subspaces

Image-based human age estimation has become one of the interesting but challenging problems in computer vision and biometrics. It is even harder when the faces have different expressions. In this paper, we propose a weighted random subspace method to solve the relatively new problem: cross-expression age estimation. The proposed method does not depend on the learning of correlation between different expressions, and thus could work in the situation when the expression-correlation does not exist in the training data. We also explore the use of data from multiple datasets to further improve the estimation performance. Experiments on two aging datasets with explicit expression changes demonstrate that the proposed approach gives superior performance over the state-of-the-art method.
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.
A study on the influence of body weight changes on face recognition

Overweight and obesity is quite common in the modern society, which can result in many severe health problems. Thus weight loss has become a major event for many people to have a healthy living. A question is then raised for Biometrics or identity management: Is there any influence on face recognition when the facial shapes are varied, caused by body weight changes? No previous research has addressed this issue, to the best of our knowledge. In this paper, we study the influence of body weight changes on face recognition. Both synthesized and real face images are assembled as the databases to facilitate our study. Empirically, we found that large body weight alterations can significantly reduce the matching accuracy of the face recognition system. This is a new exploration to the biometrics society. Then we study if the influence of weight changes can be reduced to improve the face recognition performance. The partial least squares (PLS) method is applied for this purpose. Our preliminary results show that it is feasible to develop algorithms to address the influence of facial adiposity variation on face recognition, caused by weight changes.
Simultaneous feature selection and classifier training via linear programming: a case study for face expression recognition

A linear programming technique is introduced that jointly performs feature selection and classifier training so that a subset of features is optimally selected together with the classifier. Because traditional classification methods in computer vision have used a two-step approach: feature selection followed by classifier training, feature selection has often been ad hoc using heuristics or requiring a time-consuming forward and backward search process. Moreover, it is difficult to determine which features to use and how many features to use when these two steps are separated. The linear programming technique used in this paper, which we call feature selection via linear programming (FSLP), can determine the number of features and which features to use in the resulting classification function based on recent results in optimization. We analyze why FSLP can avoid the curse of dimensionality problem based on margin analysis. As one demonstration of the performance of this FSLP technique for computer vision tasks, we apply it to the problem of face expression recognition. Recognition accuracy is compared with results using support vector machines, the AdaBoost algorithm, and a Bayes classifier.
Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database

Big Data analysis is an emerging topic in computer vision and pattern recognition. As one example problem of big data, we study semantic age labels and facial aging pattern analysis on a large database. In aging analysis, one of the great challenges is the lack of a large number of face images with ground truth age labels. Unlike many other example-based recognition problems where human annotations can be used as the ground truth labels for both training and testing, it is quite difficult to label the exact ages in face images by human annotators. An alternative is to exploit the unlabeled ages to enhance the age estimation performance. However, it is unclear whether the face images with unlabeled ages can be used or not for age estimation, and how to use the unlabeled data. In this paper, we study the two problems comprehensively under two paradigms: the semi-supervised learning and unsupervised learning for aging pattern analysis. We emphasize the importance of using ground truth age labels and a large database in order to derive a meaningful measure in the context of big data. Our study can make an impact on collecting aging patterns that is very expensive and time consuming in practice.
Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding

Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.
Human age estimation using bio-inspired features

We investigate the biologically inspired features (BIF) for human age estimation from faces. As in previous bio-inspired models, a pyramid of Gabor filters are used at all positions of the input image for the S1 units. But unlike previous models, we find that the pre-learned prototypes for the S2 layer and then progressing to C2 cannot work well for age estimation. We also propose to use Gabor filters with smaller sizes and suggest to determine the number of bands and orientations in a problem-specific manner, rather than using a predefined number. More importantly, we propose a new operator “STD” to encode the aging subtlety on faces. Evaluated on the large database YGA with 8,000 face images and the public available FG-NET database, our approach achieves significant improvements in age estimation accuracy over the state-of-the-art methods. By applying our system to some Internet face images, we show the robustness of our method and the potential of cross-race age estimation, which has not been explored by any studies before.
Automatic Classification of Teeth in Bitewing Dental Images Using OLPP

Teeth classification is an important component in building an Automated Dental Identification System (ADIS) as part of creating a data structure that guides tooth-to-tooth matching. This aids in avoiding illogical comparisons that both inefficiently consume the limited computational resources and mislead decision-making. We tackle this problem by using low computational-cost, appearance-based Orthogonal Locality Preserving Projection (OLPP) algorithm to assign an initial class, i.e. molar or premolar to the teeth in bitewing dental images. After this initial classification, we use a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and thus assign each tooth a number corresponding to its location in the dental chart. On a large dataset of bitewing films that contain 622 teeth, the proposed approach achieves classification accuracy of 89% and teeth class validation enhances the overall teeth classification accuracy to 92%.
Fusing Spatiotemporal Features and Joints for 3D Action Recognition

We present a novel approach to 3D human action recognition based on a feature-level fusion of spatiotemporal features and skeleton joints. First, 3D interest points detection and local feature description are performed to extract spatiotemporal motion information. Then the frame difference and pairwise distances of skeleton joint positions are computed to characterize the spatial information of the joints in 3D space. These two features are complementary to each other. A fusion scheme is then proposed to combine them effectively based on the random forests method. The proposed approach is validated on three challenging 3D action datasets for human action recognition. Experimental results show that the proposed approach outperforms the state-of-the-art methods on all three datasets.
Iris Extraction Based on Intensity Gradient and Texture Difference

Biometrics has become more and more important in security applications. In comparison with many other bio- metric features, iris recognition has very high recognition accuracy. Successful iris recognition depends largely on correct iris localization, however, the performance of current techniques for iris localization still leaves room for improvement. To improve the iris localization performance, we propose a novel method that optimally utilizes both the intensity gradient and texture difference. Experimental results demonstrate that our new approach gives much better results than previous approaches. In order to make the iris boundary more accurate, we present a new issue called model selection and propose a method to choose between ellipse/circle and circle/circle models. Furthermore, we propose a dome model to compute mask images and remove eyelid occlusions in the unwrapped images rather than in the original eye images with a least commitment strategy.
Digital anti-aging in face images

We study a problem called digital facial anti-aging, which aims at making human faces look younger in digital photos. This novel problem is different from the traditional age synthesis where new faces are synthesized at older ages using other example face images. In contrast, our facial anti-aging works on a single digital photo without using any other faces. The proposed system contains several modules. First, an input color face image is decomposed into three layers: face structure, aging detail, and color. Second, the specular highlight in the original face image is recovered. Third, the face structure, color, and the recovered specular highlight are combined to form an anti-aging face image. Further, the system can deal with hair coloring and eyebrow change if needed, since these factors influence human judges of ages. Our approach keeps facial identities and delivers high-quality outputs. We show that the anti-aging system can be built based on adapting and integrating the state-of-the-art methods that were originally proposed to solve other problems. The anti-aging faces are evaluated by humans to demonstrate the performance.
Bayesian learning, global competition, and unsupervised image segmentation

A novel approach to unsupervised stochastic model-based image segmentation is presented, and the problems of parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding to different classes, a global competition strategy is adopted for label commitment based on the "power-value" associated with each sample (or site). The smaller the value, the more powerful the sample to compete. Parameter estimation and image segmentation are executed in the same process. Bayesian modeling of images by Markov random fields (MRF) makes it easy to represent the power of each site for competition. The new procedure for unsupervised image segmentation is performed on synthetic and real images to show its success.
Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition

Availability of handy RGB-D sensors has brought about a surge of gesture recognition research and applications. Among various approaches, one shot learning approach is advantageous because it requires minimum amount of data. Here, we provide a thorough review about one-shot learning gesture recognition from RGB-D data and propose a novel spatiotemporal feature extracted from RGB-D data, namely mixed features around sparse keypoints (MFSK). In the review, we analyze the challenges that we are facing, and point out some future research directions which may enlighten researchers in this field. The proposed MFSK feature is robust and invariant to scale, rotation and partial occlusions. To alleviate the insufficiency of one shot training samples, we augment the training samples by artificially synthesizing versions of various temporal scales, which is beneficial for coping with gestures performed at varying speed. We evaluate the proposed method on the Chalearn gesture dataset (CGD). The results show that our approach outperforms all currently published approaches on the challenging data of CGD, such as translated, scaled and occluded subsets. When applied to the RGB-D datasets that are not one-shot (e.g., the Cornell Activity Dataset-60 and MSR Daily Activity 3D dataset), the proposed feature also produces very promising results under leave-one-out cross validation or one-shot learning.
Age Synthesis and Estimation via Faces: A Survey

Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
Learning similarity measure for natural image retrieval with relevance feedback

A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two clusters. Images inside the boundary are ranked by their Euclidean distances to the query. The scheme is called constrained similarity measure (CSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure. Two techniques, support vector machine (SVM) and AdaBoost from machine learning, are utilized to learn the boundary. They are compared to see their differences in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The CSM metric is evaluated in a large database of 10009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.
Leveraging geometry and appearance cues for recognizing family photos

Analyzing social relations through image processing is an active and emerging research topic. Family is the basic unit of a society; recognizing and categorizing family photos is an essential step towards image-based social analysis. In this paper, we propose an approach that leverages a geometric model and an appearance model for family photo detection. The geometric feature captures scene-level information with fast computation and discriminative capability, while appearance model captures and quantifies pairwise relations between individuals in a photo. Each individual model performs well for family photo recognition, and a combined approach with both appearance and geometric features can outperform the state-of-the-art method.
Unsupervised segmentation based on multi-resolution analysis, robust statistics and majority game theory

An unsupervised model-based image segmentation technique requires the model parameters for the various image classes in an observed image to be estimated directly from the image. The accuracy of the segmentation depends on the correct estimation of the parameters, as well as on the correct labeling of the pixels. In this work, the parameters are estimated by a multiresolution analysis on the histogram and a robust estimator using least median of squares. The labeling process is based on majority game theory. The method is tested in various synthetic and real images, showing its effectiveness.
A study on automatic age estimation using a large database

In this paper we study some problems related to human age estimation using a large database. First, we study the influence of gender on age estimation based on face representations that combine biologically-inspired features with manifold learning techniques. Second, we study age estimation using smaller gender and age groups rather than on all ages. Significant error reductions are observed in both cases. Based on these results, we designed three frameworks for automatic age estimation that exhibit high performance. Unlike previous methods that require manual separation of males and females prior to age estimation, our work is the first to estimate age automatically on a large database. Furthermore, a data fusion approach is proposed using one of the frameworks, which gives an age estimation error more than 40% smaller than previous methods.
A Study on Apparent Age Estimation

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.
Learning similarity measure for natural image retrieval with relevance feedback

A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two parts. Images on the positive side of the boundary are ranked by their Euclidean distances to the query. The scheme is called restricted similarity measure (RSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance based on the Euclidean distance measure. Two techniques, support vector machine and AdaBoost, are utilized to learn the boundary, and compared with respect to their performance in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The RSM metric is evaluated on a large database of 10,009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.
Linear combination representation for outlier detection in motion tracking

In this paper we show that Ullman and Basri's linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outlier detection in motion tracking with an affine camera. For this task LC can be realized either on image frames or feature trajectories, and therefore two methods are developed which we call linear combination of frames and linear combination of trajectories. For robust estimation of the linear combination coefficients, the support vector regression (SVR) algorithm is used and compared with the RANSAC method. SVR based on quadratic programming optimization can efficiently deal with more than 50 percent outliers and delivers more consistent results than RANSAC in our experiments. The linear combination representation can use SVR in a straightforward manner while previous factorization-based or subspace separation methods cannot. Experimental results are presented using real video sequences to demonstrate the effectiveness of our LC+SVR approaches, including a quantitative comparison of SVR and RANSAC.