Depth Estimation Based Automatic Pose Correction in Hand Images

In palm biometric recognition systems, the hand being at different angles and positions relative to the camera presents a critical challenge in terms of accuracy and reliability. Especially in images captured in free environments, hand misalignment can negatively affect recognition performance by making it difficult to extract biometric attributes. Perspective distortions due to camera position change the geometric structure of the hand, compromising biometric data integrity. While 3D imaging systems can offer successful solutions for pose normalization, performing a similar transformation for 2D images poses a major technical challenge. In this study, depth estimation supported plane fitting is used to remove perspective distortions in 2D images and pose normalization of the hand is obtained. The proposed method uses the information obtained from depth estimation to realign the hand image as if it were standing directly in front of the camera, improving geometric consistency. This will make the extraction of biometric features more reliable and contribute to improving the accuracy of palm recognition systems.