I-Am: Implicitly Authenticate Me—Person Authentication on Mobile Devices Through Ear Shape and Arm Gesture
提出利用接电话时的本能动作（耳形和手臂动作）进行多模态身份认证，实验表明该方法在自然场景下有效，并公开了包含100多名受试者的数据集。
Today, identity verification is required in many common activities, and it is arguably true that most people would like to be authenticated in the easiest and most transparent way, without having to remember a personal identification number. To this regard, this paper presents a multibiometric system based on the observation that the instinctive gesture of responding to a phone call can be used to capture two different biometrics, namely ear and arm gesture, which are complementary due to their, respectively, physical and behavioral nature. We conducted a comprehensive set of experiments aimed at assessing the contribution of each of the two biometrics as well as the advantage in their fusion to the system's overall performance. Experiments also provide objective measurement of both saliency and correlation of data captured by each sensor involved (accelerometer, gyroscope, and camera) according to various features extraction, features matching, and data-fusion techniques. The reports provide evidences about the potential of the proposed system and method for user authentication “in-the-wild,” whilst its eventual usage for person identification is also investigated. All of the experiments have been carried out on a specifically built, publicly available ear-arm database, including multibiometric captures of more than 100 subjects performed during different sessions, that represents an additional contribution of this paper.