Kyu-Dae Ban

Appearance-based Face Recognition from robot camera images with Illumination and Distance Variations

This paper is concerned with the appearance-based face recognition from robot camera images with illumination and distance variations. The approaches used in this paper consist of eigenface, fisherface, and icaface, which are the most representative recognition techniques frequently used in conjunction with face recognition. These approaches are based on a popular unsupervised and supervised statistical technique that supports finding useful image representations, respectively. Thus we focus on the performance comparison from robot camera images with unwanted variations. The comprehensive experiments are completed for two face databases with illumination and distance variations. A comparative analysis demonstrates that ICA comes with improved classification rates when compared with other approaches such as eigenface and fisherface

Face image registration methods using Normalized Cross Correlation

In the many processing stage of face recognition, one of the most important parts is face detection. In the recent years, there has been much progress in face detection. Many face recognition system adopt or develop the Adaboost: Viola-Jones face detection system. But the detected face image by adaboost method has serious problems. Face regions are apt to be different at each time, and the detected face image includes the rotated face. These issues give the bad effect to the feature extraction stage of the face recognition. In order to solve these problems, many researchers normalize the scale of face through the detection of detail element of a face, especially the eye detection. Generally, the home service robot mounts the low resolution camera. As the distance between a robot and a user becomes increasing, it is very difficult to obtain the good result by those methods. In this paper, Normalized Cross Correlation is used to detect the exact face region in the low resolution face image. The experiments showed that our method using NCC give the much better face recognition rate.

Fusion Technique for User Identification Using Camera and Microphone in the Intelligent Service Robots

In this paper, the user identification based on face and speaker information obtained from camera and microphone for the intelligent service robot is proposed. For this purpose, we use fisherface method for face recognition. The choice of the fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. Furthermore, we utilize Gaussian Mixture Model (GMM) classifier which uses a Mel-Frequency Cepstral Coefficients (MFCC) as feature vector for speaker recognition. The weighted sum method is used to fuse cosine similarity and log- likelihood produced from fisherface and GMM classifier, respectively. The experimental results reveal that the presented fusion method showed a better performance than fisherface and GMM classifier itself through the research robot platform called WEVER developed in ETRI.

Multimodal user identification in a intelligent robot environment

The attempt to recognize a user through the single recognition expert like the face recognition and speaker recognition is actively comprised. But these single recognition techniques are difficult to cover the user recognition perfectly since the real environment in which a robot is positioned include the variations of the illumination and noise, and etc. In this paper, the fusion technique using face and speaker information is proposed. The proposed approach is concerned with the aggregation of the individual classifiers by means of the fuzzy integral. This stage includes two steps such as (a) the determination of the fuzzy sets described the classification results of each classifier, and (b) the fuzzy measure itself which reflects the relevance of the classifiers. The experimental results obtained for the ETRI-HRI databases reveal that the approach presented in this study yields better classification performance in comparison to the results produced by other classifiers and fusion methods.

A perception framework for supporting robots to recognize human better in Human-Robot interaction

This paper describes the reason that perception technology in HRI has not given high performance enough to be used for commercial service robots. As a practical solution for better performance of perception technology, we propose a perception framework with a dedicated perception engine called a perception demon. The demon in the proposed framework constantly collects evidences and analyses them better by combining various types of individual perception components. The proposed framework enables robot makers to easily get more reliable information on humans without concerns about optimizing perception components to their robots.