FIGO: Fingerprint Identification Approach Using GAN and One Shot Learning Techniques

Fingerprint evidence plays an important role in criminal investigations for the identification of individuals. The performance of traditional Automatic Fingerprint Identification System (AFIS) depends on the presence of valid minutiae points and still requires human expert assistance in feature extraction and identification stages. Based on this motivation, we propose a Fingerprint Identification approach based on a Generative adversarial network and One-shot learning techniques (FIGO). Our solution contains two components: (a) fingerprint enhancement tier and (b) fingerprint identification tier. First, we propose a Pix2Pix model to transform low-quality fingerprint images into higher levels of fingerprint images. Furthermore, we develop another existing solution based on Gabor filters as a benchmark to compare with the proposed model by observing the fingerprint device's recognition accuracy. Experimental results show that our proposed Pix2pix model has better support than the baseline approach for fingerprint identification. Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification tier. Using the proposed method, it is possible to learn necessary information from one training sample with high accuracy.