Lisa Kleinjohann

Real-Time Facial Expression Recognition Using a Fuzzy Emotion Model

This paper presents the fuzzy video based emotion recognition system VISBER, that allows to analyze facial expressions in video sequences. In order to process images in real-time a tracking mechanism is used for face localization. The fuzzy classification itself analyzes the deformation of a face separately in each image. In contrast to most existing approaches, also blended emotions with varying intensities as proposed by psychologists can be handled. For this purpose we propose a fuzzy emotion model which is generally applicable for also for other emotion recognition solutions. Furthermore, VISBER supports the automatic adaptation to the characteristics of individual human faces by a short training phase that can be done before the emotion recognition starts.

Fuzzy emotion recognition in natural speech dialogue

This paper describes the realization of a natural speech dialogue for the robot head MEXI with focus on its emotion recognition. Specific for MEXI is that it can recognize emotions from natural speech and also produce natural speech output with emotional prosody. For recognizing emotions from the prosody of natural speech we use a fuzzy rule based approach. Since MEXI often communicates with well known persons but also with unknown humans, for instance at exhibitions, we realized a speaker-dependent mode as well as a speaker-independent mode in the prosody based emotion recognition. A key point of our approach is that it automatically selects the most significant features from a set of twenty analyzed features based on a training data base of speech samples. This is important according to our results, since the set of significant features differs considerably between the distinguished emotions. With our approach we reached average recognition rates of 84% in speaker-dependent mode and 60% in speaker-independent mode.

Prosody based emotion recognition for MEXI

This paper describes the emotion recognition from natural speech as realized for the robot head MEXI. We use a fuzzy logic approach for analysis of prosody in natural speech. Since MEXI often communicates with well known persons but also with unknown humans, for instance at exhibitions, we realized a speaker dependent mode as well as a speaker independent mode in our prosody based emotion recognition. A key point of our approach is that it automatically selects the most significant features from a set of twenty analyzed features based on a training database of speech samples. This is important according to our results, since the set of significant features differs considerably between the distinguished emotions. With our approach we reach average recognition rates of 84% in speaker dependent mode and 60% in speaker independent mode.

Feature based Face Localization and Recognition on Mobile Devices

Due to increasing miniaturization and decreasing prizes of cameras more and more mobile devices like PDAs or smartphones are equipped with a camera. Due to this fact, mobile face recognition will gain popularity in identifying persons e.g. in order to prevent unauthorized use or access to data and equipment. In this paper, the feature based face localization and recognition system FaceScry is presented. In spite of the limited resources available on mobile devices, FaceScry is able to localize an arbitrary number of faces with different sizes in images taken under varying illumination conditions in real-time. Also face recognition is size invariant due to the selected set of features, which mainly consists of angles and cross ratios. Since it stores reference face data for recognition as feature vectors and not as huge image data, FaceScry also allows for keeping a reasonable personal face data base for recognizing a set of persons on the smartphone

Design and Analysis of Distributed Embedded Systems

The main purpose of this paper is to discuss if the Unified Modeling Language (UML) can be used as a system-level language (SLL) for specifying embedded systems. in co-design environments. The requirements that a language has to fulfil to be considered as an SLL are presented and the advantages and disadvantages of using UML as an SLL are also indicated. The contribution of this paper consists on the explicit discussion of the key issues that must be taken into account when deciding if UML is to be used in a project as an SLL for embedded software.

A distributed multi-UAV path planning approach for 3D environments

During rescue scenarios it is indispensable to obtain an overview of the situation. Unmanned aerial vehicles (UAVs) can gather the necessary information in a fast and efficient way. This paper presents an approach for path planning in 3D environments offering a solution to explore disaster areas including, e. g., partially or completely destroyed buildings. Using multiple UAVs decreases the time needed to receive a complete overview if the problem of coordination and task allocation is solved. We present an approach for the use of multiple UAVs. The UAVs work in a distributed manner without any central coordination instance and cover the exploration of terrains as well as goal-oriented path planning. When using multiple UAVs redundant exploration is avoided through the use of inter-UAV-communication. The approach is based on potential fields and uses the simplicity of the gradient method to calculate paths for fast exploration of the terrain.

Combining Autonomous Exploration, Goal-Oriented Coordination and Task Allocation in Multi-UAV Scenarios

Successful rescue operations after big accidents or natural disasters require a fast and efficient overview of the overall situation. With recent advances, unmanned aerial vehicles (UAVs) are more and more a viable choice under such circumstances.With the number of employed UAVs, the problem of coordination arises as well as proper task allocation among possibly heterogeneous UAVs. This paper presents a hybrid approach for UAV coordination and covers the exploration of unknown terrains as well as goal-oriented coordination and simultaneous task allocation. The approach combines the simplicity of the gradient method with informed A* search and supports prioritized task assignment. The system is suited for highly dynamic environments requiring frequent path recalculations.

Integration of emotional reactions on human facial expressions into the robot head MEXI

Emotion recognition and adequate reactions are a crucial part of human communication and hence should also be considered for interactions between humans and robots. In this paper we present the robot head MEXI which is able to recognize emotions of its human counterpart from a video sequence using a fuzzy rule based approach. It reacts on these perceptions in an emotional way. Therefor MEXI maintains an internal state made up of (artificial) emotions and drives. This internal state is used to evaluate its perceptions and action alternatives and controls its behavior on the basis of this evaluation. This is a major difference between MEXI and usual goal based agents that rely on a world model to control and plan their actions. For MEXI the behavior based programming paradigm originally developed by Arkin for robot navigation was extended to support a multidimensional control architecture based on emotions and drives.

Learning service recommendations

The as a service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilised on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our approach of modelling this service composition and recommendation process as Markov decision process and of solving it by means of reinforcement learning. A case study serves as proof of concept.

Learning action sequences through imitation in behavior based architectures

In this paper a new architecture for learning action sequences through imitation is proposed. Imitation occurs by means of observing and applying sequences of basic behaviors. When an agent has observed another agent and applied the observed action sequence later on, this imitated action sequence can be seen as a meme. Agents that behave similarly can therefore be grouped by their typical behavioral patterns. This paper thus explores imitation from the view of memetic proliferation. Combining imitation learning with meme theory we show by simulating agent societies that with imitation significant performance improvements can be achieved. The performance is quantified by using an entropy measure to qualitatively evaluating the emerging clusters. Our approach is demonstrated by the example of a society of emotion driven agents that imitate each other to reach pleasant emotional state.