Comparison of Different Neural Networks for Iris Recognition: A Review
Shivani Godara, Rajeev Kumar Gupta · 2012
Biometrics is the science of verifying the identity of an individual through physiological measurements or behavioral traits. Since biometric identifiers are associated permanently with the user they are more reliable than token or knowledge based authentication methods. Among all the biometric modalities, iris has emerged as a popular choice due to its variability, stability and security. In this paper, we are presenting the various iris recognition techniques and its learning algorithm with neural network. Implementation of various techniques can be standardized on dedicated architectures and learning algorithm. It has been observed that SOM has stronger adaptive capacity and robustness. HSOM, which is based on hamming distance, has improved accuracy over LSOM. SANN model is more suitable in measuring the shape similarity, while cascaded FFBPNN are more reliable and efficient method for iris recognition.