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Limited data availability is a challenging problem in the latent fingerprint domain. Synthetically generated fingerprints are vital for training data-hungry neural network-based algorithms. Conventional methods distort clean fingerprints to generate synthetic latent fingerprints. We propose a simple and effective approach using style transfer and image blending to synthesize realistic latent fingerprints. Our evaluation criteria and experiments demonstrate that the generated synthetic latent fingerprints preserve the identity information from the input contact- based fingerprints while possessing similar characteristics as real latent fingerprints. Additionally, we show that the generated fingerprints exhibit several qualities and styles, suggesting that the proposed method can generate multiple samples from a single fingerprint. 

In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting. 

Using fingerphoto images acquired from mobile cameras, low-quality sensors, or crime scenes, it has become a challenge for automated identification systems to verify the identity due to various acquisition distortions. A significant type of photometric distortion that notably reduces the quality of a fingerphoto is the blurring of the image. This paper proposes a deep fingerphoto deblurring model to restore the ridge information degraded by the image blurring. As the core of our model, we utilize a conditional Generative Adversarial Network (cGAN) to learn the distribution of natural ridge patterns. We perform several modifications to enhance the quality of the reconstructed (deblurred) fingerphotos by our proposed model. First, we develop a multi-stage GAN to learn the ridge distribution in a coarse-to-fine framework. This framework enables the model to maintain the consistency of the ridge deblurring process at different resolutions. Second, we propose a guided attention module that helps the generator to focus mainly on blurred regions. Third, we incorporate a deep fingerphoto verifier as an auxiliary adaptive loss function to force the generator to preserve the ID information during the deblurring process. Finally, we evaluate the effectiveness of the proposed model through extensive experiments on multiple public fingerphoto datasets as well as real-world blurred fingerphotos. In particular, our method achieves 5.2 dB, 8.7%, and 7.6% improvement in PSNR, AUC, and EER, respectively, compared to a state-of-the-art deblurring method.