Pattern recognition solves the problem of fake biometric detection. . It includes components such as a sensor module for input acquisition, a feature extraction module, a matching module for comparing features against the database, and Jun 9, 2020 · Security agencies frequently use biometric traits for automatic recognition of a person. To solve this problem, this research employs Convolutional Neural Networks (CNNs) for next-generation biometric security Abstract To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. This method is easy to use and can be used in real-time. Fake fingerprint recognition needs to pose a significant problem in the quickly developing field of biometric security. Abstract: Recently, individual biometric characteristics gained a lot of attention and are the heart of building multiple kinds of security and authenticity systems, such as surveillance, forensic, fraudulent disclosing, and identity-based access control. It is intended to increase security. The methods of detection of deepfakes have rapidly advanced in the use of ML and DL models to identify minute differences in a manipulated media clip. It involves preprocessing data, extracting features, selecting important features, training a model using machine learning algorithms, and classifying new data. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. Jun 3, 2025 · Detection techniques have become an integral part of security in biometric data-based systems, including face, voice, fingerprint, and iris recognition. The vast types of biometrics traits make the procedure of selecting the suitable one a crucial issue, which mainly depends on the type of Pattern recognition is the process of classifying data based on knowledge gained from patterns in training data. This system adds liveness assessment to biometric recognition systems in an easy-to-use, non-invasive way. The key To ensure that the object presented in front of biometric device is real or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. This paper, presents a software-based fake biometric detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts Biometric recognition is defined as a process that involves enrolling a user by capturing biometric characteristics, extracting relevant features, and storing them in a template database for future identification or verification. Mar 1, 2024 · This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. Biometric systems are vulnerable to fake attacks, and many studies have been done to solve it. Abstract—The fingerprint recognition system is the most used among various biometric methods. In this paper Genetic algorithm (GA) is used with 25 General Image quality features for biometric fake detection using Face, Fingerprint and Iris. The creation of creative and effective spoof detection systems has become crucial as weaknesses in traditional authentication methods have been revealed. Therefore, in this paper, we propose a fake fingerprint detection method using neural network. However, in recent years, different types of spoofing attacks are used to violate the security of a biometric system. Common applications include image processing, computer vision, speech recognition, and fingerprint identification. Biometrics liveness detection system used to recognize Oct 25, 2024 · And while AI spoofing of authentication systems using fingerprint recognition, iris scanning, and voice recognition – the most widely used biometric methods – may be possible, newer biometrics such as vein pattern recognition and heart rate sensors could prove to be less capable of being forged, and would likely require an AI capability There are various search techniques among which Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines . In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect This paper describes a software-based fake detection method that can be used in multiple biometric systems to detect fraudulent access attempts. The human iris is the most hopeful biometric authentication that can accurately identify a person from their exclusive features. onuh 2igmx kjzv bd 5rk7b z6r7 xcgzw ikcc qbigvr sj