Despite the tremendous need for the evaluation of touch-based authentication as an extra security layer for mobile devices, the huge disparity in the experimental methodology used by different researchers makes it hard to determine how much research in this area has progressed. Critical variables such as the types of features and how they are pre-processed, the training and testing methodology and the performance evaluation metrics, to mention but a few, vary from one study to the next. Additionally, most datasets used for these evaluations are not openly accessible, making it impossible for researchers to carry out comparative analysis on the same data. This paper takes the first steps towards bridging this gap. We evaluate the performance of ten state-of-the-art touch-based authentication classification algorithms under a common experimental protocol, and present the associated benchmark dataset for the community to use. Using a series of statistical tests, we rigorously compare the performance of the algorithms, and also evaluate how the 'failure to enroll' phenomena would impact overall system performance if users exceeding certain EERs were barred from using the system. Our results and benchmark dataset open the door to future research that will enable the community to better understand the potential of touch gestures as a biometric authentication modality.