Multimodal Person Recognition for Human-Vehicle Interaction
Erzin, E., Yemez, Y., Tekalp, A. M., Ercil, A., Erdogan, H., Abut, H.
Over the past 30 years, the field of biometric person recognition-recognizing individuals according to their physical and behavioral characteristics- has undergone significant progress. Next-generation human-vehicle interfaces will likely incorporate biometric person recognition, using speech, video, images, and analog driver behavior signals to provide more efficient and safer vehicle operation, as well as pervasive and secure in-vehicle communication. Yet, technical and deployment limits hamper these systems’ ability to perform satisfactorily in real-world settings under adverse conditions. For instance, environmental noise and changes in acoustic and microphone conditions can significantly degrade speaker recognition performance. Similarly, factors such as illumination and background variation, camera resolution and angle, and facial expressions contribute to performance loss in visually identifying a person. Biometric person recognition in vehicles is especially likely to challenge researchers because of difficulties posed by the vehicle’s interior compartment as well as by economics. In this article, we present an overview of multimodal in-vehicle person recognition technologies. We demonstrate, through a discussion of our proposed framework, that the levels of accuracy required for person recognition can be achieved by fusing multiple modalities. We discuss techniques and prominent research efforts, and we present the results of two case studies we conducted. The sidebar, "Solutions for In-Vehicle Person Recognition," discusses related work.