An Iterative Approach for EEG-Based Rapid Face Search: A Refined Retrieval by Brain Computer Interfaces

Recent face recognition techniques have achieved remarkable successes in fast face retrieval on huge image datasets. But the performance is still limited when large illumination, pose, and facial expression variations are presented. In contrast, the human brain has powerful cognitive capability to recognize faces and demonstrates robustness across viewpoints, lighting conditions, even in the presence of partial occlusion. This paper proposes a closed-loop face retrieval system that combines the state-of-the-art face recognition method with the powerful cognitive function of the human brain illustrated in electroencephalography signals. The system starts with a random face image and outputs the ranking of all of the images in the database according to their similarity to the target individual. At each iteration, the single trial event related potentials (ERP) detector scores the user´s interest in rapid serial visual presentation paradigm, where the presented images are selected from the computer face recognition module. When the system converges, the ERP detector further refines the lower ranking to achieve better performance. In total, 10 subjects participated in the experiment, exploring a database containing 1,854 images of 46 celebrities. Our approach outperforms existing methods with better average precision, indicating human cognitive ability complements computer face recognition and contributes to better face retrieval.