Gait is among the most dependable, accurate, and secure methods of biometric identification. However, high power consumption and low computing capability are two major obstacles on wearable sensors-based gait recognition system. In this work, an integrated system is reported combining a triboelectric nanogenerator (TENG), a memristor (Ag/HfOx/Pt), and perovskite-based multicolor LEDs (PMCLED) for the visualization and recognition of foot patterns through signal-on-none and multi-wavelength on-device preprocessing. The flexible TENG acts as a sensory receptor, generating voltage based on the duration and intensity of pressure, which in turn promotes voltage-triggered synaptic plasticity in the memristor. The PMCLED, with its threshold switching and multi-wavelength emission characteristics, enables nonlinear filtering and amplification of the synaptic signal from the memristor, resulting in a simplified system design and reduced background noise. Additionally, the effectiveness of on-device preprocessing is validated based on a 5 × 5 array of integrated devices and software-built neural network for foot pattern visualization and recognition. The proposed system is able to recognize the on-device preprocessed images with high accuracy, indicating great potentials in both healthcare monitoring and human-machine interaction.