7461874
Face and Facial Expression Recognition with an Embedded System for Human-Robot Interaction
In this paper, we present an embedded system in which face recognition and facial expression recognition for Human-Robot Interaction are implemented. To detect face with a fast and reliable way, AdaBoost algorithm is used. Then, Principal Component Analysis is applied for recognizing the face. Gabor wavelets are combined with Enhanced Fisher Model for facial expression recognition. Performance of the facial expression recognition reaches to 93%. The embedded system runs on 150MHz and the processing speed is 0.6 frames / second. Experimental result demonstrates that face detection, recognition and facial expression can be implemented with an embedded system for the Human-Robot Interaction.
33400437
Facial Expression Recognition Using Active Appearance Model
This paper describes a facial expression recognition system based upon Active Appearance Model (AAM), which has been typically used for the face recognition task. Given that AAM has been also used in tracking the moving object, we thought it could be effective in recognizing the facial expressions of humans. Our results show that the performance of the facial expression recognition using AAM is reliably high when it combined with an enhanced Fisher classification model.
37111410
Performance Comparison Between Backpropagation, Neuro-Fuzzy Network, and SVM
In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
33372839
3-D Facial Expression Recognition-Synthesis on PDA Incorporating Emotional Timing
This paper describes a pipeline by which facial expression of the face is recognized and then 3-D facial animation is synthesized in the remote place based upon timing information of the facial. The system first detects a facial area within the given image and then classifies its facial expression into 7 emotional weightings. Such weighting information, transmitted to the PDA via a mobile network, is used for non-photorealistic facial expression animation. A cartoon-like shading is used to render a 3-D avatar that conveys a familiar and yet unique facial character, even without employing extensive polygons. It turns out that facial expression animation using emotional curves is more effective in expressing the timing of an expression comparing to the linear interpolation method. The emotional avatar embedded on a mobile platform has some potential in conveying emotion between peoples in Internet.
20557981
3-D Pose Tracking of the Car Occupant
Although airbags in the car play an important role for the safety of occupants, in fact, many peoples have been injured or killed by the deployment of airbags themselves. Such conventional airbags are deployed by the shock sensors. As an alternative approach, a vision-based smart airbag system could be promising. This paper describes a new method by which 3-D pose of the car occupant can be recognized. We combine 2-D head tracking information with a disparity map of the occupant for 3-D pose tracking. Result shows that the system can locate the head position around the passenger's seat with a real-time basis.
15690106
Genetic Search For Fixed Channel Assignment Problem With Limited Bandwidth
We propose a hybrid genetic algorithm for the fixed channel assignment problem with limited bandwidth. A local optimization algorithm was devised to enhance the genetic algorithm's fine-tunning. In a matrix representation, the local optimization algorithm improves on a chromosome by horizontal and vertical moves of channels. The crossover is directly performed on two-dimensional matrices. Experimental results showed dramatic improvement against previous works.
344302
New topologies for genetic search space
We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed.
47302704
On the performance of group key agreement protocols
Group key agreement (GKA) is a fundamental building block for securing peer group communication systems (GCS). Several group key agreement protocols were proposed in the past, all assuming an underlying group communication infrastructure. This paper presents a performance evaluation of 5 notable GKA protocols integrated with a reliable group communication system (Spread). They are: centralized group key distribution (CKD), Burmester-Desmedt (BD), Steer et al. (STR), group Diffie-Hellman GDH) and tree-based group Diffie-Hellman (TGDH).. We present concrete results obtained in experiments on local- and wide-area networks. Our analysis of these results offers insights into their relative scalability and practicality.
18399312
Power-Conscious Configuration Cache Structure and Code Mapping for Coarse-Grained Reconfigurable Architecture
Coarse-grained reconfigurable architecture aims to achieve both performance and flexibility. However, power consumption is no less important for the reconfigurable architecture to be used as a competitive processing core in embedded systems. In this paper, we show how power is consumed in a typical coarse-grained reconfigurable architecture. Based on the power breakdown data, we suggest a power-conscious configuration cache structure and code mapping technique, which reduce power consumption without performance degradation. Experimental results show that the proposed approach saves much power even with reduced configuration cache size
19448510
A Genetic Approach for Gene Selection on Microarray Expression Data
Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of genes exceeds the number of samples, class prediction on microarray expression data leads to an extreme “curse of dimensionality” problem. A principal goal of these studies is to identify a subset of informative genes for class prediction to reduce the curse of dimensionality. We propose a novel genetic approach that selects a subset of predictive genes for classification on the basis of gene expression data. Our genetic algorithm maximizes correlation between genes and classes and minimizes intercorrelation among genes. We tested the genetic algorithm on leukemia data sets and obtained improved results over previous results.