This paper proposes an intelligent 2ν-support vector machine-based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images.
The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each sub-band of the image. The match score and the corresponding quality score of an image are fused using 2ν-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms. (Published Abstract Provided)
Similar Publications
- Comparing the Uses and Benefits of Stationary Cameras Versus Body-worn Cameras in a Local Jail Setting
- ChatGPTing Securely: Using Machine Learning to Automate Writing Rape Reports, Closed Source Large Language Models
- Cognitive Behavioral Interventions and Misconduct Behind Bars: A Randomized Control Trial of CBI-CC