Chunghoon Kim

Shadow compensation in 2D images for face recognition

Illumination variation that occurs on face images degrades the performance of face recognition. In this paper, we propose a novel approach to handling illumination variation for face recognition. Since most human faces are similar in shape, we can find the shadow characteristics, which the illumination variation makes on the faces depending on the direction of light. By using these characteristics, we can compensate for the illumination variation on face images. The proposed method is simple and requires much less computational effort than the other methods based on 3D models, and at the same time, provides a comparable recognition rate.

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.

Image covariance-based subspace method for face recognition

This paper proposes a new subspace method that is based on image covariance obtained from windowed features of images. A windowed input feature consists of a number of pixels, and the dimension of input space is determined by the number of windowed features. Each element of an image covariance matrix can be obtained from the inner product of two windowed features. The 2D-PCA and 2D-LDA methods are then obtained from principal component analysis and linear discriminant analysis, respectively, using the image covariance matrix. In the case of 2D-LDA, there is no need for PCA preprocessing and the dimension of subspace can be greater than the number of classes because the within-class and between-class image covariance matrices have full ranks. Comparative experiments are performed using the FERET, CMU, and ORL databases of facial images. The experimental results show that the proposed 2D-LDA provides the best recognition rate among several subspace methods in all of the tests.

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.

A discriminant analysis using composite features for classification problems

In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features. It contains information on statistical dependency among multiple primitive features. A discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA, which is a desirable property especially for binary classification problems. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods based on primitive features.

A New Biased Discriminant Analysis Using Composite Vectors for Eye Detection

We propose a new biased discriminant analysis (BDA) using composite vectors for eye detection. A composite vector consists of several pixels inside a window on an image. The covariance of composite vectors is obtained from their inner product and can be considered as a generalization of the covariance of pixels. The proposed composite BDA (C-BDA) method is a BDA using the covariance of composite vectors. We construct a hybrid cascade detector for eye detection, using Haar-like features in the earlier stages and composite features obtained from C-BDA in the later stages. The proposed detector runs in real time; its execution time is 5.5 ms on a typical PC. The experimental results for the CMU PIE database and our own real-world data set show that the proposed detector provides robust performance to several kinds of variations such as facial pose, illumination, eyeglasses, and partial occlusion. On the whole, the detection rate per pair of eyes is 98.0% for the 3604 face images of the CMU PIE database and 95.1% for the 2331 face images of the real-world data set. In particular, it provides a 99.7% detection rate for the 2120 CMU PIE images without glasses. Face recognition performance is also investigated using the eye coordinates from the proposed detector. The recognition results for the real-world data set show that the proposed detector gives similar performance to the method using manually located eye coordinates, showing that the accuracy of the proposed eye detector is comparable with that of the ground-truth data.

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.

Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System

We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.

Dimensionality reduction based on ICA for regression problems

In manipulating data such as in supervised learning, we often extract new features from the original features for the purpose of reducing the dimensions of feature space and achieving better performance. In this paper, we show how standard algorithms for independent component analysis (ICA) can be applied to extract features for regression problems. The advantage is that general ICA algorithms become available to a task of feature extraction for regression problems by maximizing the joint mutual information between target variable and new features. Using the new features, we can greatly reduce the dimension of feature space without degrading the regression performance.

Confidence Measure Using Composite Features for Eye Detection in a Face Recognition System

We propose a new confidence measure to evaluate the eye detection results and combine two different eye detectors. The confidence for the results of eye detection is measured by the distances from the test sample and the positive samples, where the distance is calculated in the composite feature space. By using the proposed confidence measure, we construct a hybrid detector by combining two different detectors, which are complementary to each other. The experimental results show that the proposed detector provides more accurate eye detection results and consequently results in better face recognition rates compared to when using an individual eye detector.