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Capacitive swipe gestures have recently emerged as a promising behavioral biometric modality for user authentication and identification on smartphones. While prior studies have demonstrated their effectiveness using classical machine learning techniques, the potential of deep learning in this domain remains largely unexplored, particularly under limited training data conditions. In this paper, we investigate the use of deep learning for capacitive swipe–based user identification and address the challenge of data scarcity through an image fusion strategy. Experiments on a dataset of 30 users show that the proposed "deep learning plus image fusion" approach significantly improves performance over a standalone deep learning baseline and achieves results comparable to state-of-the-art classical machine learning methods. These findings highlight the importance of data size and demonstrate the potential of image fusion for enhancing deep learning–based user identification.