Eye movement information is the key clue for recognizing the vision-dominated tasks, such as browsing the web, or watching a video. However, traditional wearable sensors are invasive and the vision-based eye trackers are very expensive and need time consuming calibration. Therefore, an activity recognition method based on eye movement analysis under one web camera is first proposed and the feasibility is assessed. First, an iris tracking method for the low quality image is proposed to acquire eye movement information. Then, five ten novel features are extracted from the horizontal and the vertical eye movement signals for activity recognition, and the optimal feature subset is selected. Finally, the support vector machine is used to assess the feasibility of the proposed method. Three experiments are designed for different applications: leave-one-out cross-validation, k-fold cross-validation, and validation after respective calibration. Experimental results show that their accuracies are 68.4%, 79.3% and 84.1%, respectively, which demonstrate the promise of eye based activity recognition using one web camera.