Article
Image Quality Assessment for Fake Biometric detection
In biometric authentication systems, distinguishing between genuine traits and fake or artificially generated samples is a critical challenge, requiring the development of effective security mechanisms. This paper presents a novel software-based fake detection approach that can be applied across multiple biometric systems to identify fraudulent access attempts. The primary goal of the proposed system is to improve the security of biometric recognition frameworks by incorporating liveness detection in a fast, user-friendly, and non-intrusive manner using image quality assessment techniques. The method is designed with low computational complexity, making it suitable for real-time applications. It utilizes 25 general image quality features extracted from a single captured image—the same image used for authentication—to differentiate between genuine and fake samples. Experimental results on publicly available datasets, including fingerprint, iris, and 2D face images, demonstrate that the proposed method performs competitively compared to existing state-of-the-art techniques. The study highlights that analyzing general image quality provides valuable information, enabling efficient and accurate discrimination between authentic biometric traits and spoofed samples.
Full Text Attachment
