AMCOA: Agent Motion Control With Obstacle Avoidance for Efficient Palm ROI Extraction

Palm recognition facilitates various real-world applications such as security access control and identity verification. The contactless method of acquiring palm images significantly reduces the risk of bacterial transmission and enhances user-friendliness. However, this approach also presents inherent difficulties and challenges. Accurately identifying the region of interest (ROI) is crucial for feature extraction and recognition. The absence of strict guidelines regarding the position and angle of the user’s palm during contactless image acquisition complicates the accurate localization and extraction of the ROI. To address this issue, we propose an innovative ROI extraction method that utilizes Agent Motion Control with Obstacle Avoidance (AMCOA). The agent dynamically identifies finger valley points while navigating and avoiding obstacles in the image, enabling the effective extraction of both the palmprint and palm vein ROIs based on the positions of two keypoints within the finger valleys. Our method is implemented in a synchronous acquisition system designed for palmprint and palm vein ROIs. We use a sensor module that simultaneously captures the distance, infrared (IR) image, and visible light (VL) image of the hand. The IR image is used to segment the hand area and extract the palm veins ROI, while the VL image aids in the extraction of the palmprint ROI after proper alignment. Experimental results indicate that our proposed method achieves both accurate and efficient ROI extraction.