Blockchain and Machine Learning for Advanced Pattern Recognition in Biometric Security Systems

Blockchain and machine learning allow biometric security systems to recognize complex patterns. This study analyzes how two unique technologies may improve fingerprint system safety and reliability. To demonstrate blockchain and machine learning in biometrics. The main reason for this study is that fingerprint protection is becoming more important in today’s digital world. There needs to be strong name verification right away because there is more sensitive data and privacy issues. Traditional biometric systems have come a long way, but there are still problems with data security, privacy, and being able to grow. This study suggests important worries about how blockchain technology and machine learning might be able to improve pattern recognition, make security measures stricter, make data unchangeable, and reach the level of scalability needed for widespread use. Upon careful study of related approaches and works, biometric security could be improved by combining the decentralized and unchangeable record of blockchain with the pattern recognition abilities of machine learning. Ten important ways are looked at, and each one has its own pros and cons. A trio of CNNs, SVMs, and RNNs are used in the method we suggest. The suggested blockchain and machine learning fusion is shown to be better in a detailed analysis. The study ends with compared tables that show the parameters and performance evaluations of $\mathbf{1 0}$ similar systems.