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The recent shift towards contactless fingerprint sensors requires precise fingertip segmentation with changing backgrounds, to maintain high accuracy. This study introduces a novel deep learning model combining ResNeSt and UNet++ architectures called FingerUNeSt++, aimed at improving segmentation accuracy and inference speed for contactless fingerprint images. Our model significantly outperforms traditional and state\u2010of\u2010the\u2010art methods, achieving superior performance metrics. Extensive data augmentation and an optimized model architecture contribute to its robustness and efficiency. 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