Abstract
Machine Facial Recognition is a problem that consists in exploring still or video images of a scene to identify or verify one or more persons in the scene using stored databases of faces. Research in Facial Recognition is multidisciplinary including disciplines like pattern recognition, image processing and analysis, computer graphics, machine learning and data mining. Sometimes applications include not only facial recognition but also facial expression characteristics identification and classification like blinking of the eyes, open/close mouth among others. This paper presents a comparative study of three algorithms for classification basic facial expressions.

Abstract
This paper presents the evaluation of computational mind model based on temperamental decision algorithms with emotional behaviors. Our computational model of emotion is inspired on appraisal theory and on superior nervous system characteristics. We define the model for temperamental agent with emotions. In this paper we prove that teams of the agents with different temperaments have different performances in the same simulation scenario. The result shows that strategies based on temperamental decision mechanism strongly influence system performance and there are evident dependencies between emotional states of agents and their temperamental type, as well as dependencies between the team performance and team configuration, and this enables us to conclude that modular approach to emotional programming based on temperamental theory is a good choice to develop computational mind models for emotional behavioral Multi-Agent systems.

Abstract
Human observation and memory are not reliable enough to provide accurate and objective information from athletes in high-performance environments. In this context analysis tools constitute an important instrument for professional coaches, as they provide information on player's behavior statistically compiled. Most of the tools presented for the soccer universe, offer non-real time information and, with the hardware devices being quite costly. In many cases these systems (specially camera based solutions) also present some technical issues mainly in what concerns occlusion and computational demands. This research work presents a real time soccer analysis system based on a Wi-Fi location approach that can be used by a soccer coach in a training session or even a match. Using a Wi-Fi network, inexpensive tags and a positioning engine on top of it, this portable system provides a visualization tool for such data on a real time basis. The results are quite satisfactory, because this system was able to track the players during a training exercise and display this data on a real time bases and it was also able to provide information on players' movements and their interaction all over the exercise.

Abstract
Both the academic and industrial worlds have increased investment and dedication to the affective computing area in the past years. At the same time, immersive environments have become more and more a reliable domain, with progressively cheaper hardware and software solutions. With this in mind, the authors used biometric readings to perform real-time user emotion assessment in an immersive environment. In the example used in this paper, the environment consisted in a flight simulation, and biometric readings were based on galvanic skin response, respiration rate and amplitude, and phalanx temperature. The detected user emotional states were also used to modify some simulation variables, such as flight plan, weather and maneuver smoothness. The emotion assessment results were consistent with user-described emotions, achieving an overall success rate of 78%.

Abstract
Affective computing and multichannel multimedia distribution have gathered the time and investment of industry and academics. The proposed system merges such domains so that ubiquitous system can be enhanced through online user emotion assessment based on user's biosignals. It was used IAPS as a emotional library for controlled visual stimuli and biosignals were collected in real-time - heartbeat rate and skin conductance - in order to online assess the user's emotional state through Russell's Circumplex Model of Affect. In order to improve usability and session setup, a distributed architecture was used so that software models might be physically detached. The conducted experimental sessions and the validation interviews supported the system's efficiency not only in real-time discrete emotional state assessment but also considering the emotion inducing process. The next logic step consists in replicating the achieved success in multi-format multimedia contents without the need of pre-defined restricted emotional metadata.

Abstract
Recently topics such as affective computing and multichannel multimedia distribution have gained the attention and investment of both industry and academics. The proposed system joins these domains so that ubiquitous system can be potentiated by means of online user emotion assessment based on real-time user's biosignals. It was used IAPS as a emotional library for controlled visual stimuli and biosignals were collected in real-time - heartbeat rate and skin conductance - in order to online assess the user's emotional state through Russell's Circumplex Model of Affect. To improve usability and session setup, a distributed architecture was used so that software models might be physically detached. The conducted experimental sessions and the validation interviews supported the system's efficiency not only in real-time discrete emotional state assessment but also considering the emotion inducing process. The future work consists in replicating the success in multi-format multimedia contents without pre-defined emotional metadata.