Incremental template updating for face recognition in home environments
The extreme variability of faces in smart environment applications, due to continuous changes in terms of pose, illumination and subject appearance (hairstyle, make-up, etc.), requires the relevant mode of variations of the subject's faces to be encoded in the templates and to be continuously updated based on new inputs. This work proposes a new video-based template updating approach suitable for home environments where the image acquisition process is totally unconstrained but a large amount of face data is available for continuous learning. A small set of labeled images is initially used to create the templates and the updating is then totally unsupervised. Although the method is here presented in conjunction with a subspace-based face recognition approach, it can be easily adapted to deal with different kinds of face representations. A thorough performance evaluation is carried out to show the efficacy and reliability of the proposed technique.
template updating home environment video-based face recognition subspace learning