Joonwhoan Lee

Fuzzy-connective-based hierarchical aggregation networks for decision making

Abstract Decision making is usually based on the aggregation of several criteria. Although the aggregation can be performed at one level in simple cases, in more complex situations it becomes necessary that it be performed in a hierarchical manner in a multilevel framework. This is particularly true in many engineering applications. In this paper, we investigate the use of fuzzy connectives in multilayer networks to arrive at appropriate overall aggregation functions. The characteristics of suitable fuzzy connectives are studied and an iterative algorithm to determine the type of aggregation function and its parameters at each node in the network is proposed. We believe that our aggregation scheme is powerful and flexible, since we use several types of aggregation connectives and since the parameters of the network can be determined using a simple algorithm. The behavior of the network can also be modified depending on the desired ‘attitude’. Conditions for unique convergence of the algorithm in simple cases are discussed, and examples of applications of the method are also presented.

Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

Nonlinear transfer function-based local approach for color image enhancement

The main objective of image enhancement is to improve some characteristic of an image to make it visually better one. This paper proposes a method for enhancing the color images based on nonlinear transfer function and pixel neighborhood by preserving details. In the proposed method, the image enhancement is applied only on the V (luminance value) component of the HSV color image and H and S component are kept unchanged to prevent the degradation of color balance between HSV components. The V channel is enhanced in two steps. First the V component image is divided into smaller overlapping blocks and for each pixel inside the block the luminance enhancement is carried out using nonlinear transfer function. In the second step, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel value and its neighborhood pixel values. Finally, original H and S component image and enhanced V component image are converted back to RGB image. The subjective and objective performance evaluation shows that the proposed enhancement method yields better results without changing image original color in comparison with the conventional methods.

A Robust Face Detection Method Based on Skin Color and Edges

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non- face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions

Antibacterial activity of hydroxyalkenyl salicylic acids from sarcotesta of Ginkgo biloba against vancomycin-resistant Enterococcus

A chloroform fraction prepared from the sarcotesta of Ginkgo biloba showed potent inhibitory activity against vancomycin-resistant Enterococcus (VRE). The active compounds were elucidated to be 2-hydroxy-6-(8-pentadecenyl) salicylic acid (1) and 2-hydroxy-6-(10-heptadecenyl) salicylic acid (2) based on their spectral analysis. Compounds 1 and 2 showed significant antibacterial activities against VRE.

Facial expression recognition based on local region specific features and support vector machines

Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.

Emotional Evaluation of Color Patterns Based on Rough Sets

If the emotion that a man or woman feels seeing color patterns in average sense can be extracted as rules, the result is useful to make an emotion-based color image retrieval system. This paper shows that the rough set theory provides a convenient tool for the purpose. We collect the emotion data when people see a set of predesigned random color patterns and extract the coarse rules for the emotional evaluation of the color patterns using VPRS (Variable Precision Rough Set) theory. Those rules can be used not only to approximately evaluate color patterns such as wall papers but also to set the initial conditions for the precise mapping system based on adaptive fuzzy logic from image features to emotion spaces represented by linguistic image scales.

Object tracking in MPEG compressed video using mean-shift algorithm

A new tracking scheme of an object in MPEG compressed domain is proposed in this paper. In the scheme, the motion flow for each macro block is obtained from the motion vectors included in the MPEG video stream, and the simple camera operation is robustly estimated using generalized Hough transform. Then, the global camera operation is used to compensate the motion flow to determine the object motions. The residual motion flow after compensation is treated as a feature of moving objects for tracking. In the paper, we used mean-shift algorithm based on the residual motion flow rather than color information as Meer did in the uncompressed video. The experimental results show the validity of the proposed scheme.

Recognition of facial expressions based on salient geometric features and support vector machines

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets.

Fuzzy Similarity-Based Emotional Classification of Color Images

This paper proposes a novel scheme for evaluating an emotional response to color images. The proposed scheme uses case-based reasoning in which the prototypical color images for each emotion are stored as cases and are compared with the images to be evaluated. In the comparison, the similarities in terms of image descriptors play an important role, and their combination is crucial for the construction of a proper similarity measure. In the training phase of the proposed scheme, the weights that represent the unequal importance of each descriptor is determined in order to obtain a similarity measure that can be used to evaluate and classify a color image with respect to a pair of emotions. Prior to classification, the representative color images are chosen for each emotion by human subjects and are stored as cases. The stored images are compared with an image to be classified using the constructed similarity measure to determine which emotion is appropriate between a pair of emotions. In this study, we used color and texture descriptors recommended by MPEG-7, represented as high-dimensional vectors. In the training, we proposed a method based on the rough approximation and the fuzzy inter- and intra-similarities to determine the weights that represent the unequal importance of the complex MPEG-7 descriptors. Experimental results show a promising performance for the proposed scheme, and better performance could be achieved by including more prototypical images as cases.