In human activity classification, detecting speaking activity can be applied further in behavior analysis such as student learning behavior in an active learning environment. This paper presents a method for classifying whether or not a person is speaking based on lib movement in a video sequence. Assuming that a person of interest is tracked within a room using multiple cameras, at least one camera can capture the face of a target person at every instant of time. Using this sequence of frames of a target person, this paper proposes a method for continuously deciding whether the person is speaking. Firstly, head part is segmented based on (1) the head's top position, (2) head's width and golden ratio of head's height and width. Secondly, the face area is extracted using a skin detection technique. Thirdly, the mouth area in each frame is segmented based on its geometry on a face and a mouth has different color from face skin. Next, mouth opening is roughly detected based on the fact that the opening area has a darker gray level than its average. Finally, only frequency components between 1 Hz to 10 Hz of the detected feature signal is extracted and used to classify the speaking activity by comparing with a threshold. The proposed method is tested with 3 sets of videos. The results showed that the speaking classification and mouth detection achieved 93 % and 94 % accuracy, respectively.