A haptic texture database for tool-mediated texture recognition and classification

While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. In this paper, we introduce a haptic texture database which allows for a systematic analysis of feature candidates. The publicly available database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures. As a preliminary feature analysis, we test and compare six well-established features from audio and speech recognition together with a Gaussian Mixture Model-based classifier on our recorded free hand signals. Among the tested features, best results are achieved using Mel-Frequency Cepstral Coefficients (MFCCs), leading to a texture recognition accuracy of 80.2%.