Abstract
Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.
Highlights
We propose a new measurement method to evaluate the quality of generated images for biometric recognition systems based on the use of a conventional face-presentation attack detection (PAD) system and the dprime measurement
As explained in our above sections, our study purpose is to generate presentation attack (PA) images those are close to the captured PA images, not detecting PA images
We proposed a method for generating PA face images for face-PAD systems
Summary
Biometric models, such as fingerprints, faces, irises, and finger-vein models, have been widely used in high-performance systems for recognizing/identifying a person [1,2]. These recognition systems offer more convenience to users than conventional recognition methods, such as token- and knowledge-based methods [1]. Face-based recognition systems are popular biometric recognition systems and have been used for a long time to recognize people [3,4,5] This type of biometric is based on the fact that facial appearance can be used to distinguish people.
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