Jiyong Oh

Combined subspace method using global and local features for face recognition

This paper proposes a combined subspace method using both global and local features for face recognition. The global and local features are obtained by applying the LDA-based method to either the whole or part of a face image, respectively. The combined space is constructed with the projection vectors corresponding to large eigenvalues of the between-class scatter matrix in each subspace. It is based on the fact that the eigenvectors corresponding to larger eigenvalues have more discriminating power. The combined subspace is evaluated in view of the Bayes error, which shows how well samples can be classified. The combined subspace gives small Bayes error than the subspaces composed of either the global or local features. Comparative experiments are also performed using the color FERET database of facial images. The experimental results show that the combined subspace method gives better recognition rate than other methods.

Feature extraction for one-class classification problems: Enhancements to biased discriminant analysis

In many one-class classification problems such as face detection and object verification, the conventional linear discriminant analysis sometimes fails because it makes an inappropriate assumption on negative samples that they are distributed according to a Gaussian distribution. In addition, it sometimes cannot extract sufficient number of features because it merely makes use of the mean value of each class. In order to resolve these problems, in this paper, we extend the biased discriminant analysis (BDA) which was originally developed for one-class classification problems. The BDA makes no assumption on the distribution of negative samples and tries to separate each negative sample as far away from the center of positive samples as possible. The first extension uses a saturation technique to suppress the influence of the samples which are located far away from the decision boundary. The second one utilizes the L1 norm instead of the L2 norm. Also we present a method to extend BDA and its variants to multi-class classification problems. Our approach is considered useful in the sense that without much complexity, it successfully reduces the negative effect of negative samples which are far away from the center of positive samples, resulting in better classification performances. We have applied the proposed methods to several classification problems and compared the performance with conventional methods.

Input variable selection for feature extraction in classification problems

We propose an input variable selection method based on discriminant features. By analyzing the relationship between the input space and feature space obtained by discriminant analysis, the input variables that contain a large amount of discriminative information are selected, while input variables with less discriminative information are discarded. By this, the signal to noise ratio of the data can be improved. The proposed method can be applied not only to the feature extraction methods based on covariance matrix but also to the methods based on image covariance matrix. The experimental results obtained with various data sets show that the proposed method results in improved classification performance regardless of the dimension and type of data.

Generalized mean for robust principal component analysis

In this paper, we propose a robust principal component analysis (PCA) to overcome the problem that PCA is prone to outliers included in the training set. Different from the other alternatives which commonly replace L2-norm by other distance measures, the proposed method alleviates the negative effect of outliers using the characteristic of the generalized mean keeping the use of the Euclidean distance. The optimization problem based on the generalized mean is solved by a novel method. We also present a generalized sample mean, which is a generalization of the sample mean, to estimate a robust mean in the presence of outliers. The proposed method shows better or equivalent performance than the conventional PCAs in various problems such as face reconstruction, clustering, and object categorization. HighlightsWe propose a robust principal component analysis.The generalized mean is used in the proposed method instead of the arithmetic mean.A novel method is also presented to solve our optimization problem.

Selective generation of Gabor features for fast face recognition on mobile devices

In this paper, we propose a robust face recognition method to provide fast response on a mobile device by selectively generating Gabor features. The Gabor filter has been popularly used in face recognition to improve recognition performance. Since the computational effort for generating a Gabor feature is very large, it is important to use only the discriminative Gabor features on mobile devices which do not have sufficient computing power. At the same time, it is also important to maintain the recognition performance at an acceptable level. To reduce computational effort without degrading the recognition performance, the proposed method selectively generates Gabor features based on a contribution measure obtained by discriminant analysis. Face recognition is performed using only the selectively generated Gabor features, and the experimental results for the CMU Multi-PIE database and a real world data set show that the number of Gabor features can be reduced by more than 50% while keeping almost the same recognition performance. On a 624MHz mobile phone, the execution time of feature extraction can be reduced to 19ms from 46ms on average.

Generalized mean for feature extraction in one-class classification problems

Biased discriminant analysis (BDA), which extracts discriminative features for one-class classification problems, is sensitive to outliers in negative samples. This study focuses on the drawback of BDA attributed to the objective function based on the arithmetic mean in one-class classification problems, and proposes an objective function based on a generalized mean. A novel method is also presented to effectively maximize the objective function. The experimental results show that the proposed method provides better discriminative features than the BDA and its variants.

Team SNU's Control Strategies for Enhancing a Robot's Capability: Lessons from the 2015 DARPA Robotics Challenge Finals

This paper presents the technical approaches used and experimental results obtained by Team SNU Seoul National University at the 2015 DARPA Robotics Challenge DRC Finals. Team SNU is one of the newly qualified teams, unlike 12 teams who previously participated in the December 2013 DRC Trials. The hardware platform THORMANG, which we used, has been developed by ROBOTIS. THORMANG is one of the smallest robots at the DRC Finals. Based on this platform, we focused on developing software architecture and controllers in order to perform complex tasks in disaster response situations and modifying hardware modules to maximize manipulability. Ensuring stability and modularization are two main keywords in the technical approaches of the architecture. We designed our interface and controllers to achieve a higher robustness level against disaster situations. Moreover, we concentrated on developing our software architecture by integrating a number of modules to eliminate software system complexity and programming errors. With these efforts on the hardware and software, we successfully finished the competition without falling, and we ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the 2015 DRC Finals.

Team SNU’s Control Strategies to Enhancing Robot’s Capability: Lessons from the DARPA Robotics Challenge Finals 2015

This paper presents the technical approaches used and experimental results obtained by Team SNU at the DARPA Robotics Challenge (DRC) Finals 2015. Team SNU is one of the newly qualified teams, unlike the 12 teams who previously participated in the December 2013 DRC Trials. The hardware platform THORMANG, which we used, has been developed by ROBOTIS. THORMANG is one of the smallest robots at the DRC Finals. Based on this platform, we focused on developing software architecture and controllers in order to perform complex tasks in disaster response situations and modifying hardware modules to maximize manipulability. Ensuring stability and modularization are two main keywords in the technical approaches of the architecture. We designed our interface and controllers to achieve a higher robustness level against disaster situations. Moreover, we concentrated on developing our software architecture by integrating a number of modules to eliminate software system complexity and programming errors. With these efforts on the hardware and software, we have successfully finished the competition without falling and ranked 12th out of 23 teams. This paper is concluded with a number of lessons learned by analyzing the DRC Finals 2015.

Robust PCAs and PCA Using Generalized Mean

In this chapter, a robust principal component analysis (PCA) is described, which can overcome the problem that PCA is prone to outliers included in training set. Different from the other alternatives which commonly replace \(L_{2}\)-norm by other distance measures, our method alleviates the negative effect of outliers using the characteristic of the generalized mean keeping the use of the Euclidean distance. The optimization problem based on the generalized mean is solved by a novel method. We also present a generalized sample mean, which is a generalization of the sample mean, to estimate a robust mean in the presence of outliers. The proposed method shows better or equivalent performance than the conventional PCAs in various problems such as face reconstruction, clustering, and object categorization.

Online recognition of handwritten music symbols

In this paper, we propose an effective online method to recognize handwritten music symbols. Based on the fact that most music symbols can be regarded as combinations of several basic strokes, the proposed method first classifies all the strokes comprising an input symbol and then recognizes the symbol based on the results of stroke classification. For stroke classification, we propose to use three types of features, which are the size information, the histogram of directional movement angles, and the histogram of undirected movement angles. When combining classified strokes into a music symbol, we utilize their sizes and spatial relation together with their combination. The proposed method is evaluated using two datasets including HOMUS, one of the largest music symbol datasets. As a result, it achieves a significant improvements of about 10% in recognition rates compared to the state-of-the-art method for the datasets. This shows the superiority of the proposed method in online handwritten music symbol recognition.