  Perceptual processes are frequently multi-modal. This is the case of haptic
perception. Data sets of visual and haptic sensory signals have been compiled
in the past, especially when it comes to the exploration of textured surfaces.
These data sets were intended to be used in natural and artificial perception
studies and to provide training data sets for machine learning research. These
data sets were typically acquired with rigid probes or artificial robotic
fingers. Here, we collected visual, auditory, and haptic signals acquired when
a human finger explored textured surfaces. We assessed the data set via machine
learning classification techniques. Interestingly, multi-modal classification
performance could reach 97% when haptic classification was around 80%.
