Haptic object recognition is widely used in various robotic manipulation tasks. Using the shape features obtained at either a local or global scale, robotic systems can identify objects solely by touch. Most of the existing work on haptic systems either utilizes a robotic arm with end-effectors to identify the shape of an object based on contact points, or uses a surface capable of recording pressure patterns. In this work, we introduce a novel haptic capture system based on the local curvature of an object. We present a haptic sensor system comprising of three aligned and equally spaced fingers that move towards the surface of an object at the same speed. When an object is touched, our system records the relative times between each contact sensor. Simulating our approach in a virtual environment, we show that this new local and low-dimensional geometrical feature can be effectively used for shape recognition. Even with 10 samples, our system achieves an accuracy of over 90 % without using any sampling strategy or any associated spatial information.