Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network.