Synthetic Fingerprint Generation: Bridging the Gap Between Privacy and Security with Variational Auto-Encoders

This study presents a state-of-the-art technique utilizing Variational Auto-encoders (VAEs) for synthetic fingerprint generation, aiming to strike a balance between privacy and security in biometric systems. The proposed VAE architecture employs an encoder-decoder network to transform raw fingerprint data into a lower-dimensional latent space, enabling the generation of diverse and realistic synthetic fingerprints. The workflow involves encoding raw data, mapping it to the latent space, and then decoding it to produce detailed synthetic representations. The model’s architecture is elucidated, detailing the encoder and decoder components, and their respective parametrizations. The reparameterization trick is employed to address gradient computation challenges during training. The training process involves the minimization of both reconstruction loss and Kullback-Leibler divergence loss, ensuring the generated fingerprints maintain fidelity to the input while adhering to a standard normal distribution in the latent space. The study demonstrates the effectiveness of the proposed VAE through comprehensive results, including reconstruction and KL divergence loss curves. Synthetic fingerprint images are showcased, illustrating the model’s ability to faithfully reconstruct input data. Additionally, the model’s capability to generate diverse synthetic fingerprints by manipulating latent vectors is highlighted. The generated imaged is checked with NFIQ-2 for checking the quality of the image.