OPTIMIZATION OF HIDDEN MARKOV MODEL PARAMETERS FOR BIOMETRIC SIGNATURE SYSTEMS - Bharadi, Vinayak A.; Chavan, Manoj (ShodhKosh: Journal of Visual and Performing Arts, 2022)
Hidden Markov Models (HMMs) have long been employed for biometric sequence modeling due to their robustness in capturing temporal dynamics. This paper presents a focused parametric analysis on how the selection of HMM parameters—specifically, the number of hidden states, observation symbols, and training samples—influences the Equal Error Rate (EER) in online signature verification systems. Using MATLAB simulations and the SVC 2004 dataset, we systematically vary these parameters while maintaining consistent preprocessing and feature extraction. Our findings highlight optimal parameter ranges that yield the best trade-off between model complexity and verification accuracy, with 4–5 hidden states and 300–350 observation symbols providing peak performance. These insights are intended to guide the design of efficient and accurate signature verification architectures.