Feedback-optimized ghost recognition with human visual perception

Conventional target recognition approaches typically depend on large-scale datasets and computationally intensive electronic processing, which inherently limits processing speed and raises data privacy concerns. In contrast, optical computing supports ultra-high speed parallel processing, and the human visual system can directly perceive integrated light field information. Motivated by this, we propose a human-perceptible ghost recognition scheme with feedback optimization. Ghost recognition extends the principle of ghost imaging by directly recognizing targets from the bucket signal, thereby circumventing the conventional image reconstruction step. The system employs an all-optical diffractive deep neural network (D²NN) to perform feature extraction, mapping the incident wavefront onto a predefined light field intensity distribution. By leveraging the visual persistence of the human eye, the time-integrated light signal can be directly recognized without electronic post-processing. The proposed system achieves 90.72% recognition accuracy, validating its feasibility in practical scenarios. This work highlights the potential of combining optical computing with visual perception to develop efficient recognition systems.