As the growth of smart home, healthcare, and home robot applications, learning a face recognition system which is specific for a particular environment and capable of self-adapting to the temporal changes in appearance (e.g., caused by illumination or camera position) is nowadays an important topic. In this paper, given a video of a group of people, which simulates the surveillance video in a smart home environment, we propose a novel approach which unsuper- visedly learns a face recognition model based on two main components: (1) a triplet network that extracts identity-aware feature from face images for performing face recognition by clustering, and (2) an augmentation network that is conditioned on the identity-aware features and aims at synthesizing more face samples. Particularly, the training data for the triplet network is obtained by using the spatiotemporal characteristic of face samples within a video, while the augmentation network learns to disentangle a face image into identity-aware and identity-irrelevant features thus is able to generate new faces of the same identity but with variance in appearance. With taking the richer training data produced by augmentation network, the triplet network is further fine-tuned and achieves better performance in face recognition. Extensive experiments not only show the efficacy of our model in learning an environment- specific face recognition model unsupervisedly, but also verify its adaptability to various appearance changes.

In the paper1, we propose a rule-based mobile agent generation framework for information retrieval in a flexible, transparent, and easy way. User can submit a flat-text based request, the request will be automatically deduced by a Reasoning Agent (RA) based on predefined ontology and inference rule, and then be translated to a Mobile Information Retrieving Agent Description File (MIRADF) that is formatted in a proposed Mobile Agent Description Language (MADF). A generating agent, named MIRA-GA, is also implemented to generate a MIRA according to the MIRADF. We also design and implement a prototype to integrate these agents and show an interesting example to demonstrate the feasibility of the architecture.

In this paper, we propose an agent and ontology based information retrieval architecture for mobile grid, named AIR, to discover and extract from mobile grid environment in a flexible, transparent, and easy way. Based on the AIR, users can submit a flat-text based request to mobile grid for discovering information or knowledge. The request will be automatically reasoned by a reasoning agent based on ontology and reasoning rule, and be translated to a mobile information retrieving agent (MIRA) that can be migrated to mobile grid node to retrieve information that users require. In order to generate a mobile IR agent, we propose a mobile agent description language (MADL) that defines the behavior of mobile IR agent. In addition, a knowledge synthesis agent in the architecture is designed to collect and synthesize information from mobile grids. Finally, we show an interesting example to demonstrate the feasibility of the architecture.