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Shoulder-surfing is the act of spying on an authorized user of a computer system with the malicious intent of gaining unauthorized access. Current solutions to address shoulder-surfing such as graphical passwords, gaze input, tactile interfaces, and so on are limited by low accuracy, lack of precise gaze-input, and susceptibility to video analysis attack. We present an intelligent gaze gesture-based system that authenticates users from their unique gaze patterns onto moving geometric shapes. The system authenticates the user by comparing their scan-path with each shapes' paths and recognizing the closest path. In a study with 15 users, authentication accuracy was found to be 99% with true calibration and 96% with disturbed calibration. Also, our system is 40% less susceptible and nearly nine times more time-consuming to video analysis attacks compared to a gaze- and PIN-based authentication system.