1.1 The Drawbacks of Traditional Security Technologies and the Challenges They Face
Modern science and technology, especially information technology, has significantly advanced society, offering faster and more convenient communication methods for people. At the same time, it has brought new importance to the management of countries and societies, focusing on how to verify the identity of each individual in a timely and accurate manner.
Traditional authentication methods rely on verifying that a person possesses a valid document or token. Essentially, this method checks an object rather than the person themselves. As long as the object is valid, the person's identity is considered confirmed. This approach has several vulnerabilities:
1. If a legitimate user loses their verification object (like a password or key), they cannot be verified anymore.
2. Forged documents, tokens, or passwords can be stolen or deciphered, allowing unauthorized individuals to pass as legitimate users. For example, criminals may enter restricted areas using fake documents, or employees may use someone else’s card to punch in at work.
3. Losing a key not only prevents access but also poses risks if someone else finds it and uses it to steal property.
Many computer systems still use the "user ID + password" method for authentication. However, this system has inherent issues. Passwords are easy to forget, and once lost, users cannot access the system. Surveys show that forgotten passwords are one of the most common IT support issues. Moreover, stolen passwords pose even greater threats. Unauthorized individuals could access confidential data, commit fraud, or withdraw money from bank accounts. It is relatively simple for others to observe keystrokes or guess personal information like birthdays or phone numbers. These vulnerabilities make passwords highly insecure.
Even highly secure systems, such as U.S. military networks, have been hacked multiple times, with attackers gaining access to legitimate user credentials. Although changing passwords periodically helps prevent theft, it increases the user's memory burden without fully solving the problem. Table 1.1 shows the financial losses caused by authentication fraud in the U.S. in 1996.
These problems highlight the challenges traditional security technologies face. Despite being convenient, they are no longer sufficient in the networked world where e-commerce is growing rapidly. People now seek more secure and reliable ways to confirm identities online. Biometric features, which are unique and hard to replicate, have become a hot topic in security research.
1.2 Introduction to Biometric Systems
Biometric identification involves using physiological characteristics or behavioral traits of the human body to authenticate a person's identity. Since these features are unique and hard to copy, they offer a much safer alternative to passwords or tokens. To be effective, biometric systems must meet certain criteria:
- Universality: Every person should have the feature.
- Uniqueness: Each person's feature should be distinct.
- Stability: The feature should remain consistent over time.
- Collectability: The feature should be easily obtainable and quantifiable.
In practice, additional factors must be considered, such as system performance, user acceptance, and privacy concerns. A practical biometric system should be user-friendly, easy to collect, accurate, and resistant to deception.
Figure 1.1 illustrates a generic biometric system, which consists of two modules: registration and authentication. During registration, the system captures and stores biometric data. In authentication, the system compares the captured data with stored templates to verify identity.
Biometric systems can be categorized into verification and identification. Verification involves comparing a single biometric sample with a registered template, while identification matches the sample against a database of many templates. Both methods have different requirements in terms of speed and accuracy.
Each biometric system has a certain level of confidence, which can lead to two types of errors: false rejection (FRR) and false acceptance (FAR). Balancing these errors is crucial for practical applications. High-security systems may prioritize minimizing FAR, while general systems may focus on reducing FRR.
The ROC curve is commonly used to evaluate system performance, with the equal error rate (ERR) being a key metric. Faster matching speeds are particularly important for large-scale identification systems.
1.3 Comparison of Several Biometric Technologies
Biometric technologies can be based on either physiological features or behavioral traits. Physiological features, such as facial structure, fingerprints, and iris patterns, are generally more reliable due to their stability and uniqueness. Behavioral traits, such as voice and signature, are also used but tend to be less accurate.
Common biometric technologies include face recognition, fingerprint recognition, palm shape recognition, hand vein recognition, iris recognition, retinal pattern recognition, facial thermal imaging, ear recognition, signature recognition, and voiceprint analysis. Among these, face, fingerprint, palm, hand veins, iris, facial thermal image, ear, and retinal patterns are physiological features, while signature and voice are behavioral traits.
Face recognition is intuitive and widely used, with static and dynamic modes. Dynamic recognition faces more challenges due to environmental changes. Fingerprint recognition is well-established, with high accuracy and reliability. Palm shape recognition is simple and cost-effective but less reliable for large populations. Hand vein recognition offers good stability, though its uniqueness is still under study. Iris recognition is highly accurate but requires specialized equipment and can be uncomfortable for users.
Table 1.2 summarizes the advantages and disadvantages of various biometric technologies, highlighting fingerprint recognition as a strong candidate due to its uniqueness, stability, and accuracy.
1.4 Fingerprint Identification Technology
This paper aims to design an automated personal identity authentication system using fingerprint recognition. Fingerprint recognition offers several advantages: uniqueness, stability, widespread acceptance, affordability, and efficient storage. Fingerprint templates are derived from detailed features, making them suitable for remote identification and digital signatures.
An automatic fingerprint identification system (AFIS) involves three key steps: image acquisition, processing, and matching. Modern sensors, such as Veridicom’s FPS200, use optical, ultrasonic, and capacitive technologies to capture high-quality images. Image processing algorithms, including enhancement and feature extraction, are critical for accurate recognition.
Fingerprint matching relies on minutiae points such as endpoints and bifurcations. Various algorithms, including Hough transform and neural networks, are used for matching. However, current systems still face challenges in achieving high accuracy and fast processing.
1.5 The Task of This Paper
Given the limitations of traditional security technologies, there is a growing interest in biometrics. Fingerprint recognition, despite its advantages, lacks a unified standard. This paper aims to develop a better fingerprint image processing and recognition algorithm for the Veridicom FPS200 sensor. The goal is to achieve a low misrecognition rate and fast matching time for small civilian databases. Key challenges include preprocessing, post-processing, and applying recognition techniques effectively.
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