Randomized trees for real-time one-step face detection and recognition

We present a system for detecting and recognizing faces in images in real-time which is able to learn new identities in instants. In mobile service robotics, interaction with persons is becoming increasingly important, real-time performance is required and the introduction of new persons is a necessary feature for many applications. Although face detection and face recognition are well studied, only a few papers address both problems jointly and only few systems are able to learn to identify new persons quickly. To achieve real-time performance on modest computing hardware, we use random forests for both detection and recognition, and compare with well-known techniques such as boosted face detection and support vector machines for identification. Results are presented on different datasets and compare favorably well to competitive methods.