Real‐Time Iris Recognition With Stand‐Alone Embedded Processor Based on AI Model

ABSTRACT This study focuses on iris recognition using deep learning techniques. The EfficientDet network model was employed for both iris detection and recognition tasks. Four datasets were utilized to train and evaluate the deep learning network. The model was trained to extract iris features and classify individuals based on their unique iris patterns. The proposed method achieved a high recognition rate of over 98% across multiple dataset evaluations. For real‐time implementation on an embedded system, the trained model was quantized to an 8‐bit integer format to accommodate resource‐constrained devices. Despite this quantization, the recognition accuracy remained high, reaching 97%. By incorporating an Edge TPU accelerator alongside a Raspberry Pi system, the processing speed reached up to 10 frames per second during real‐time iris camera testing, demonstrating the feasibility of real‐time iris recognition. An intruder test was conducted to assess the system's robustness in preventing unauthorized access. The False Acceptance Rate (FAR) was measured to assess the likelihood of incorrectly accepting an unauthorized individual. Experimental results show that the FAR can be reduced to zero by applying additional temporal constraints, effectively preventing unauthorized individuals from passing the iris recognition‐based access control system.