Existing circular object detection methods can be broadly classified into voting-based and maximum likelihood estimation (MLE) based. The former is robust to noise, however its computational complexity and memory requirement are high; MLE-based methods (e.g., robust least squares fitting) are more computationally efficient but sensitive to noise, and can not detect multiple circles. This study proposes Probabilistic Pairwise Voting (PPV), a fast and robust algorithm for circular object detection based on an extension of Hough Transform. We formulate the problem of circular object detection as finding the intersection of lines in the three dimensional parameter space (i.e., center and radius of the circle). We propose a probabilistic pairwise voting scheme to robustly discover circular objects, and use a mode-finding algorithm to efficiently find multiple circular objects. We show the benefits of our approach on two real-world problems: 1) detecting circular objects in natural images, and 2) localizing iris in face images.