For several years, keyboard and mouse have been used as the main interacting devices between users and computer games, but they are becoming outdated. Gesture-based human-computer interaction systems are becoming more popular owing to the emergence of virtual reality and augmented reality technologies. Therefore research on these systems has attracted a significant attention. The researches focus on designing the interactive interfaces between users and computers. Human-computer interaction is an important factor in computer games because it affects not only the experience of the users, but also the design of the entire game. In this research, we develop an particle filter-based face tracking method using color distributions as features, for the purpose of applying to gesture-based human-computer interaction systems for computer games. The experimental results proved the efficiency of particle filter and color features in face tracking, showing its potential in designing human-computer interactive games.

As well as all other branches of trade, so retail trade itself undergoes various changes and trends with regard to the development of information and communication technologies which affect not only traders themselves but also their customers. It is the retail store environment itself which is one of the decisive aspects of purchase because more than 70% of consumer decisions take place directly at the point of sale. It is the last place which can reverse the purchasing decision. A final customers´ decision is influenced not only by price, quality but also by in-store communication and visual aspects of each store. That is the reason for continuous gathering of feedback on the effectiveness and efficiency of these means of communication in real environment. Besides traditional research techniques there are situations which require the involvement of relatively new research methods. Thanks to the innovative interdisciplinary approach with the use of neuromarketing, it is possible to create effective marketing strategies and thus stimulate the customer attention and emotions. By these emotions, it is possible to achieve better motivation toward purchase and an increase in the number of sales and subsequent raise in income. The paper deals with a complex, interdisciplinary examination of the in-store communication impact on customer visual attention, emotions and related spatial behaviour of customers in grocery stores. Research integrates measurements of mobile eye camera (Eye tracker), mobile electroencephalograph (EEG), face reading technology (FA) and internal position system in real conditions of retail store. The purpose of this research is to recognise the attention, emotional response and spatial customer preferences by means of selected in-store communication tools. At the end of the paper we explain how the neuromarketing methods can be used for better understanding of consumer behaviour at the point of sale.

In this paper, I present my face detection and tracking method. First, image enhancement is carried out in HSV space especially if the input image is acquired from unconstrained illumination condition. I used a method for image enhancement in HSV space based on the local processing of image. I propose a lighting invariant face detection system based upon the edge and skin tone information of the input color image. The advantage of the proposed face detection is that, it can detect faces with different size, pose, and expression under unconstrained illumination conditions. I combined the Kalman filter with Camshift to enable track recovery after occlusions and to avoid the tracking failures caused by objects and background with similar colors to face. In my tracking method, I particularly focus on face tracking. The size and position of window are obtained after Camshift iteration. Kalman filtering is used to predict the next starting iterative point of Camshift. The experimental results show that my tracking method get the better results than Camshift in occlusion sequences and dynamic backgrounds.

Finding a head of a person in a scene is very important for taking a well composed picture by a robot photographer because it depends on the position of the head. So in this paper, we propose a robust head tracking algorithm using a hybrid of an omega shape tracker and local binary pattern (LBP) AdaBoost face detector for the robot photographer to take a fine picture automatically. Face detection algorithms have good performance in terms of finding frontal faces, but it is not the same for rotated faces. In addition, when the face is occluded by a hat or hands, it has a hard time finding the face. In order to solve this problem, the omega shape tracker based on active shape model (ASM) is presented. The omega shape tracker is robust to occlusion and illumination change. However, when the environment is dynamic, such as when people move fast and when there is a complex background, its performance is unsatisfactory. Therefore, a method combining the face detection algorithm and the omega shape tracker by probabilistic method using histograms of oriented gradient (HOG) descriptor is proposed in this paper, in order to robustly find human head. A robot photographer was also implemented to abide by the 'rule of thirds' and to take photos when people smile.

In this paper, we deal with the performance evaluation method of user identification and user tracking for intelligent robots using face images. This paper shows general approaches for standard evaluation methods to improve intelligent robot systems as well as their algorithms. The evaluation methods proposed in this paper can be combined with the evaluation methods for detection algorithms of face region and facial components to measure the overall performance of face recognition in intelligent robots.