This paper introduces a neuromorphic tactile sensing system based on piezoelectric polymers for texture classification. Raw tactile signals were first converted into spike trains, from which spike-based features were extracted to train a multilayer perceptron (MLP). To improve generalization and mitigate the so-called batch effect, an optimal linear transportation was applied along with a leave-one-subject-out evaluation strategy. Experimental results show that the model trained on the transported data achieved a performance improvement of approximately 90% in accuracy compared to the original data. The proposed system demonstrates strong potential for subject-independent tactile perception.