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.

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.

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.

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.

Recently, several nonlinear shape normalization methods have been proposed in order to compensate for shape distortions in large-set handwritten characters. The authors review these methods from the two points of view: feature projection and feature density equalization. The former makes a feature projection histogram by projecting a certain feature at each point into horizontal- or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. Then, a quantitative evaluation for these methods has been made based on the following criteria: recognition rate, processing speed, computational complexity, and degree of variation.>