The capabilities and acceptance of social robots would be greatly improved by developing their ability to determine human intent. Trust, which is an important consideration in collaborations, is affected by the intent of the agents involved. Prediction of a human's intent to deceive a robot is understudied and an important factor in calculating trust. We present a method that predicts the intent to deceive in a card game scenario that relies on facial emotion recognition. Video data collected through a human study where subjects played against three opponent types: Human, Robot, and Computer simulation, was used to validate model performance in the wild. We show that our method which uses Mini-batched K-Means not only facilitates online learning, but exceeds human performance in 17 out of 30 trials. In addition, this end-to-end pipeline removes the dependency on human annotations used in a prior method allowing for deployment on a robot.