Gender recognition based on ensemble learning with selective features for service robotics applications

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.

The problem of gender recognition using visual and acoustic cues has recently received significant attention. This paper explores the use of Total Variability (i-vectors) and Inter-Session Variability (ISV) modeling techniques for both unimodal and bimodal gender recognition, and compares them to several state-of-the-art algorithms. The experimental evaluation is conducted on the FERET and LFW databases for face-based gender recognition, on the NISTSRE database for audio-based gender recognition, and on the MOBIO database for audio-visual gender recognition. Results on LFW show that the i-vectors technique outperforms state-of-the-art algorithms, which are based on Support Vector Machines (SVM) applied either on raw pixels, on Local Binary Patterns (LBP) or on Gabor filters, with an accuracy rate of about 95%. Results on NIST-SRE show that the i-vectors system is also superior to state-of-theart GMM-based gender recognition systems, with a relative gain of about 11%. Finally, results on MOBIO show that i-vectors and ISV also take advantage of combining visual and acoustic cues using logistic regression. The resulting bimodal systems achieve accuracy rates of about 98%.

An application for video data analysis based on computer vision and machine learning methods is presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 94% accuracy of viewer's gender recognition is achieved. Our age estimation algorithm provides world-quality results for MORTH database, but focused on real-life audience measurement videodata in which faces can be looks more or less similar to RUS-FD private database. In this case we can reach total mean absolute error score less than 7. All the video processing stages are united into a real-time system of audience analysis. The system allows to extract all the possible information about people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

The objective of this paper is to compare different classifiers’ recognition accuracy for the 28 Arabic alphabet letters gestured by participants as Sign Language and captured by two depth sensors. The accuracy results of three individual classifiers: (1) the support vector machine (SVM), (2) random forest (RF), and (3) nearest neighbour (kNN), using the original gestured dataset were compared with the accuracy results using an ensemble of the results of each classifier, as recommended by the literature. SVM produced higher overall accuracy when running as an individual classifier regardless of the number of observations for each letter. However, for letters with fewer than 65 observations each, which created a far smaller dataset, RF had higher accuracy than SVM did when using the ensemble approach. Although RF produced higher accuracy results for classes with limited class observation data, the difference between the accuracy results of RF in phase 2 and SVM in phase 1 was negligible. The researchers conclude that such a difference does not warrant using the ensemble approach for this experiment, which adds more processing complexity without a significant increase in accuracy.

Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem, which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the Massive Online Analysis (MOA) libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with eight state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.

This paper proposes a fusion-based gender recognition method which uses\nfacial images as input. Firstly, this paper utilizes pre-processing and a\nlandmark detection method in order to find the important landmarks of faces.\nThereafter, four different frameworks are proposed which are inspired by\nstate-of-the-art gender recognition systems. The first framework extracts\nfeatures using Local Binary Pattern (LBP) and Principal Component Analysis\n(PCA) and uses back propagation neural network. The second framework uses Gabor\nfilters, PCA, and kernel Support Vector Machine (SVM). The third framework uses\nlower part of faces as input and classifies them using kernel SVM. The fourth\nframework uses Linear Discriminant Analysis (LDA) in order to classify the side\noutline landmarks of faces. Finally, the four decisions of frameworks are fused\nusing weighted voting. This paper takes advantage of both texture and\ngeometrical information, the two dominant types of information in facial gender\nrecognition. Experimental results show the power and effectiveness of the\nproposed method. This method obtains recognition rate of 94% for neutral faces\nof FEI face dataset, which is equal to state-of-the-art rate for this dataset.\n

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying local derivative directions from the nth-order LVP in (n−1)th-order derivative space. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

This paper proposes a method for race recognition from face images using local descriptors. The proposed method uses two types of local descriptors: local binary pattern (LBP) and Weber local descriptors (WLD). First, LBP and WLD histograms are obtained separately from blocks of normalized face image. Kruskal-Wallis feature selection technique is applied to the histograms to select the significant bins for race recognition. Then the selected bins from the two histograms are concatenated block by block to produce the final feature set of the face image. Minimum city block distance is used as a classifier. The experiments are conducted using gray scale FERET images with five race groups. Experimental results show that the proposed method has superior race recognition accuracies for all the five race groups compared to LBP and WLD alone.

Ensemble classifiers are a promising approach for data stream classification. Though, diversity influences the performance of ensemble classifiers, current studies do not take advantage of relations between component classifiers to improve their performance. This paper addresses this issue by proposing a new kind of ensemble learner for data stream classification, which explicitly defines relations between component classifiers. These relations are then used in various ways, e.g., to combine the decisions of component models. The hypothesis is that an ensemble learner can yield accurate predictions in a streaming environment based on a structural analysis of a weighted network of its component models. Implications, limitations and benefits of this assumption, are discussed. A formal description of a network-based ensemble for data streams is presented, and an algorithm that implements it, named Network of Experts (NetEx). Empirical experiments show that NetEx’s accuracy and processing time are competitive with state-of-the-art ensembles.

Ensembles of extreme learning machine (ELM) have been widely used for hyperspectral image classification. The previous studies have shown that the goal of ensemble learning is to train accurate but diverse component classifiers to improve the generalization performance. To approach this goal, this article proposes a novel framework to construct an ELM ensemble model. The proposed framework relies on multiview-based random rotation ensemble pruning (MVRR-EP) and has several novel features. First, to ensure that the subsets of spectral bands can sufficiently learn the target concept, the spectral bands are divided into multiviews by using correlation analysis. Second, random rotation, a new approach of space transformation, is introduced to transform each view into multiple coordinate spaces, which makes the component classifiers trained on the transformed spaces have great diversity. Third, an accuracy guided ensemble pruning strategy is designed for pruning the component classifiers with low complementarity, and consequently, the remaining component classifiers with high complementarity are combined to a construct ensemble classifier. These techniques guarantee that the component classifiers used to construct an ensemble classifier are accurate but diverse, which ultimately improves the performance of the ensemble classifier. To demonstrate the effectiveness of the proposed MVRR-EP, extensive experiments were carried out on four hyperspectral data sets. Experimental results verify that compared with other methods, the proposed method provides competitive results.

Automatic traffic incident detection based on nFOIL