Abstract:
Biometric identification relies heavily on iris recognition systems because of
their high accuracy and dependability. In order to better understand how the degree
of freedom affects iris recognition accuracy, this study compares state-of-the-art deep
learning technique with conventional algorithms in-depth. This paper investigates the
effects of different degrees of freedom on these system’s performance, taking
precision, robustness, and adaptability into account.
This comparative study compares the state-of-the-art deep learning
approaches with classical algorithms based on well-established methodology. The
goal of the study is to shed light on the advantages and disadvantages of each
paradigm by offering a comprehensive knowledge of how various degrees of
freedom affect iris identification accuracy. The research attempts to provide
important insights into the ideal configuration of degrees of freedom for iris
recognition systems, ultimately improving their overall performance and reliability
through rigorous experimentation and methodical review.
