He Xuming, Kim Junae, Barnes Nick
NICTA, Canberra ACT, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2981-4. doi: 10.1109/EMBC.2012.6346590.
Recent studies have shown the success of face recognition using low resolution prosthetic vision, but it requires a zoomed-in and stably-fixated view, which will be challenging for a user with the limited resolution of current prosthetic vision devices. We propose a real-time object detection and tracking system capable of fixating human faces. By integrating both static and temporal information, we are able to improve the robustness of face localization so that it can fixate on faces with large pose variations. Our qualitative and quantitative results demonstrate the viability of supplementing visual prosthetic devices with the ability to visually fixate objects automatically, and provide a stable zoomed-in image stream to facilitate face and expression recognition.
最近的研究表明，使用低分辨率假视力进行人脸识别取得了成功，但这需要放大并稳定固定的视图，对于当前假视力设备分辨率有限的用户来说具有挑战性。我们提出了一种能够固定人脸的实时目标检测与跟踪系统。通过整合静态和时间信息，我们能够提高人脸定位的鲁棒性，使其能够固定在姿态变化较大的人脸上。我们的定性和定量结果证明了为视觉假体设备补充自动视觉固定物体的能力，并提供稳定的放大图像流以促进面部和表情识别的可行性。
