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In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.

Traditional e-business focuses on physical products trading, and although there are transactions of the so called "virtual products", its scope is limited to "downloadable files" or game card points. But in offline market, services & intellectual works forms an important part in all commercial fields. By reconstructing the concept of "intangible product" in e-business context, this paper discusses the definition of service & intellectual/knowledge product, gives a full description on their e-business workflow; and based on the three layered open source J2EE architecture, implements a fully functional intangible product trading platformIntelServ.com, with which service transactions can by carried out via a variety of models like B2C, C2C, bidding, auction, etc. This system can be deployed on a low cost Linux server, giving the company that hosts this project an economical startup.

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