<![CDATA[Mainstream face recognition systems have a problem regarding the disparity of recognizing faces from different races and ethnic backgrounds. This problem is caused by the imbalances in the proportion of racial representations found in mainstream datasets. Hence, the research proposes using a multi-agent system to overcome this problem. The system employs several face recognition agents according to the number of races that are necessary to make data encodings for the classification process. The first step in implementing this system is to develop a race classifier. The number of races is arbitrary or determined differently in a caseby-case scenario. The race classifier determines which face recognition agent will try to recognize the face in the query. Each face recognition agent is trained using a different dataset according to their assigned race, so they have different parts in the system. The research utilizes lazy learning algorithms as the final classifier to accommodate a system with the constant data flow of the database. The experiment divides the data into three racial groups, which are black, Asian, and white. The experiment concludes that dividing face recognition tasks based on racial groups into several face recognition models has better performance than a single model with the same dataset with the same imbalances in racial representation. The multiple agent system achieves 85% on the Face Recognition Rate (FRR), while the single pipeline model achieves only 80.83% using the same dataset.]]>
