Reliability Of Recognition System Research Articles

Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems.

Integration of multiple cues for a human authentication system

In the field of biometrics, personal authentication and identification are regarded as effective methods for automatic recognition of a person. Using multimodal biometric systems, we typically get better recognition performance compared to using a single biometric modality. We propose a multimodal biometric system using two modalities: palmprint and speech. Integrating the palmprint and speech features increases the recognition performance. We extract the discriminant features using a modified canonical form method for the Palmprint and the Mel Frequency Cepstral Coefficients (MFCC) technique for speech. The final decision is then made by fusion at the matching score level. Using a large database as the test set, the experimental results show significant improvement in reliability of recognition systems and demonstrate increases in the recognition rate.