Jeong Seon Park

Nonlinear shape normalization methods for the recognition of large-set handwritten characters

Abstract Recently, several nonlinear shape normalization methods have been proposed in order to compensate for shape distortions in large-set handwritten characters. In this paper, these methods are reviewed from the two points of view: feature projection and feature density equalization. The former makes feature projection histogram by projecting a certain feature at each point onto horizontal- or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. Then, the results of quantitative evaluation for these methods are presented. These methods have been implemented on a PC in C language and tested with a large variety of handwritten Hangul syllables. A systematic comparison of them has been made based on the following criteria: recognition rate, processing speed, computational complexity and degree of variation.

An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images

This paper proposes a face hallucination method for the reconstruction of high-resolution facial images from single-frame, low-resolution facial images. The proposed method has been derived from example-based hallucination methods and morphable face models. First, we propose a recursive error back-projection method to compensate for residual errors, and a region-based reconstruction method to preserve characteristics of local facial regions. Then, we define an extended morphable face model, in which an extended face is composed of the interpolated high-resolution face from a given low-resolution face, and its original high-resolution equivalent. Then, the extended face is separated into an extended shape and an extended texture. We performed various hallucination experiments using the MPI, XM2VTS, and KF databases, compared the reconstruction errors, structural similarity index, and recognition rates, and showed the effects of face detection errors and shape estimation errors. The encouraging results demonstrate that the proposed methods can improve the performance of face recognition systems. Especially the proposed method can enhance the resolution of single-frame, low-resolution facial images.

Glasses removal from facial image using recursive error compensation

In this paper, we propose a new method of removing glasses from a human frontal facial image. We first detect the regions occluded by the glasses and generate a natural looking facial image without glasses by recursive error compensation using PCA reconstruction. The resulting image has no trace of the glasses frame or of the reflection and shade caused by the glasses. The experimental results show that the proposed method provides an effective solution to the problem of glasses occlusion and we believe that this method can also be used to enhance the performance of face recognition systems.

Group Activity Recognition with Group Interaction Zone Based on Relative Distance Between Human Objects

In this paper, we address the problem of recognizing group activities of human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene to effectively handle noisy information. Two novel features, Group Interaction Energy (GIE) feature and Attraction and Repulsion Features, are proposed to better describe group activities within a GIZ. We demonstrate the performance of our method in two ways by (i) comparing the performance of the proposed method with the previous methods and (ii) analyzing the influence of the proposed features and GIZ-based meaningful group detection on group activity recognition using public datasets.

Automatic quality measurement of gray-scale handwriting based on extended average entropy

With a surge of interest in OCR in the 1990s, a large number of handwriting or handprinting databases have been built one after another around the world. One problem that researches encounter today is that all the databases differ in various ways including the script qualities. The paper proposes a method for measuring handwriting qualities that can be used for comparison of databases and objective test for character recognizers. The key idea involved is classifying character samples into a number of groups each characterizing a set of qualities. In order to evaluate the proposed method, we carried out experiments on the KU-1 database.

Resolution enhancement of facial image using an error back-projection of example-based learning

This work proposes a new method of enhancing the resolution of facial image from a low-resolution facial image using a recursive error back-projection of example-based learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be obtained by using the optimal coefficients for linear combination of the high-resolution prototypes, in addition to, a recursive error back-projection is applied to improve the accuracy of resolution enhancement. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.

Stepwise reconstruction of high-resolution facial image based on interpolated morphable face model

This paper proposes a new method for reconstructing a high-resolution facial image from a low-resolution facial image using stepwise reconstruction based on the interpolated morphable face model. First, we defined an interpolated morphable face model that an interpolated face is composed of a low-resolution face, its interpolated high-resolution face from a low-resolution one, and its original high-resolution one. We also proposed a stepwise reconstruction method for preventing over reconstruction caused by direct reconstruction of a high-resolution image from a low-resolution facial image. The encouraging results show that our proposed method can be used to improve the performance of face recognition systems, specifically in resolution enhancement of facial images captured on visual surveillance systems.

Resolution enhancement of facial image based on top-down learning

This paper proposes a new method of synthesizing a highresolution facial image from a low-resolution facial image based on top-down learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be synthesized by using the optimal coeffcients for linear combination of the high-resolution prototypes.The encouraging results of the proposed method show that our method can be used to increase the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at surveillance systems.

Adaptive nonlinear shape matching for unconstrained handwritten character recognition

In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.

Compositional interaction descriptor for human interaction recognition

Abstract In this paper, we address the problem of human interaction recognition. We propose a novel compositional interaction descriptor to represent complex human interactions containing high intra and inter-class variations. The compositional interaction descriptor represents motion relationships on individual, local, and global levels to build a highly discriminative description. We evaluate the proposed method using UT-Interaction and BIT-Interaction public benchmark datasets. Experimental results demonstrate that the performance of the proposed approach is on a par with previous methods.