Human detection is a key element for human-robot interaction. More and more robots are used in human environments, and are expected to react to the behavior of people. Before a robot can interact with a person, it must be able to detect it at first. This thesis presents a system for the detection of humans and their hands using a RGB-D camera. First, a model based hypotheses for possible positions of humans are created to recognize a person. By using the upper parts of the body are used to extract, new features based on relief and width of a person- head and shoulders are extracted. The hypotheses are checked by classifying the features with a support vector machine (SVM). The system is able to detect people in different poses. Both sitting and standing humans are found, by using the visible upper parts of the person. Moreover, the system is able to recognize if a human is facing or averting the sensor. If the human is facing the sensor, the color information and the distance between hand and body are used to detect the positions of the person- hands. This information is useful for gestures recognition and thus can further enhances human-robot interaction.