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Research on Image Processing in Fingerprint Recognition--Basic Term Explanation
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 for people. However, it has also raised new concerns for managers across countries and societies. The key question now is: 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 approach checks an object rather than the person themselves. If the object is valid, the person’s identity is considered confirmed. This method, however, has several vulnerabilities:
1. If a legitimate user loses their object (such as a password, key, or card), they may no longer be recognized as authorized.
2. Forged documents, tokens, or passwords can be used by unauthorized individuals to gain access. For example, criminals may enter restricted areas using fake IDs, or employees may punch in for others at work.
3. Losing a key not only prevents access but also poses a security risk if someone else finds it and uses it for theft.
Many computer systems today still use the "user ID + password" method for authentication. While convenient, this system has serious issues. Passwords are easy to forget, steal, or guess. A survey shows that forgotten passwords are one of the most common IT support problems. Moreover, stolen passwords can lead to severe consequences, such as data breaches, identity fraud, and financial loss. Even simple keystroke monitoring can reveal a password, making this method highly insecure.
Despite periodic password changes, this approach increases user burden without solving the root problem. Table 1.1 illustrates the fraud losses caused by authentication failures in the U.S. in 1996.
These challenges highlight the limitations of traditional security technologies. As society becomes more digital, people seek more reliable and secure methods of identification. Biometric systems, which use unique physical or behavioral traits, have emerged as a promising solution.
1.2 Introduction to Biometric Systems
Biometric identification involves using a person's physiological characteristics or behavioral patterns to confirm their identity. These features are unique, hard to replicate, and thus more secure than passwords or tokens. To be effective, biometric systems must meet certain criteria:
- **Universality**: Everyone 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 measurable and quantifiable.
In practice, other factors must also be considered, such as system performance, user acceptance, and privacy concerns. A practical biometric system should be user-friendly, efficient, and resistant to deception.
Figure 1.1 shows a general block diagram of a biometric system. It typically includes two modules: registration and authentication. During registration, the system captures and stores a person's biometric data. During authentication, it compares the captured data with stored templates to verify identity.
Biometric systems can be classified into verification (one-to-one matching) and identification (one-to-many matching). Verification answers, "Is this the person?" while identification answers, "Who is this?" Both approaches have different design requirements.
Like any pattern recognition system, biometric systems face errors, such as false rejection (FRR) and false acceptance (FAR). Balancing these rates is essential for real-world applications.
The ROC curve and Equal Error Rate (ERR) are commonly used to evaluate system performance. The lower the ERR, the better the system's security. Speed is also critical, especially for large-scale systems.
1.3 Comparison of Several Biometric Technologies
Biometric technologies fall into two categories: physiological and behavioral. Physiological features, such as fingerprints, facial structures, and iris patterns, are generally more reliable. Behavioral traits, like voice and signature, are easier to mimic.
Common biometric technologies include face recognition, fingerprint recognition, palm shape recognition, hand vein recognition, iris recognition, retinal scanning, facial thermal imaging, ear recognition, signature verification, and voiceprint analysis. Among these, face, fingerprint, palm, hand veins, iris, facial thermal image, ear, and retina are physiological, while signature and voice are behavioral.
Face recognition is intuitive and widely used, but dynamic recognition remains challenging. Fingerprint recognition is stable and widely accepted, making it a popular choice. Palm shape recognition is cost-effective but less reliable for large populations. Hand vein recognition is secure and difficult to forge. Iris recognition is highly accurate but requires specialized equipment. Retinal scanning is similar to iris recognition but less common due to discomfort and high cost.
Each technology has its own advantages and limitations. Fingerprint recognition stands out for its uniqueness, stability, and accuracy, making it a strong candidate for widespread use.
1.4 Fingerprint Identification Technology
This paper focuses on designing an automated personal identity authentication system using fingerprint recognition. Fingerprint recognition offers several advantages:
1. Each person’s fingerprints are unique and remain stable over time.
2. Its effectiveness is well-established and widely accepted.
3. There are many affordable fingerprint sensors available, along with standard libraries for software development.
4. Multiple fingerprints can be used to create multiple passwords, enhancing system security.
5. Fingerprint templates are derived from extracted features, reducing storage and transmission needs.
An automatic fingerprint identification system (AFIS) addresses three key challenges: image acquisition, image processing, and matching. Modern sensors, such as Veridicom’s FPS200, use various technologies, including optical, ultrasonic, and capacitive methods, to capture high-quality images.
Image processing is crucial for accurate recognition. Algorithms for direction segmentation, thinning, enhancement, and region extraction are widely used. This paper aims to develop an efficient algorithm suitable for small civilian databases, targeting a misrecognition rate of 1×10â»â¶ and a rejection rate below 10%.
1.5 The Task of This Paper
Given the limitations of traditional security systems, there is a growing need for more reliable solutions. Fingerprint recognition, despite being a mature technology, lacks a universal standard. This paper proposes an improved algorithm for fingerprint image processing and recognition, tailored for Veridicom’s FPS200 sensor. The goal is to achieve high accuracy and efficiency in a small database environment.