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Face de-identification, the process of preventing a person' identity from being connected with personal information, is an important privacy protection tool in multimedia data processing. With the advance of face detection algorithms, a natural solution is to blur or block facial regions in visual data so as to obscure identity information. Such solutions however often destroy privacy-insensitive information and hence limit the data utility, e.g., gender and age information. In this paper we address the de-identification problem by proposing a simple yet effective framework, named GARP-Face, that balances utility preservation in face deidentification. In particular, we use modern facial analysis technologies to determine the Gender, Age, and Race attributes of facial images, and Preserving these attributes by seeking corresponding representatives constructed through a gallery dataset. We evaluate the proposed approach using the MORPH dataset in comparison with several stateof-the-art face de-identification solutions. The results show that our method outperforms previous solutions in preserving data utility while achieving similar degree of privacy protection.

The effects of working memory (WM) demand and reminders on an event-based prospective memory (PM) task were compared between students with low and high achievement in math. WM load (1- and 2-back tasks) was manipulated as a within-subject factor and reminder (with or without reminder) as a between-subject factor. Results showed that high-achieving students outperformed low-achieving students on all PM and n-back tasks. Use of a reminder improved PM performance and thus reduced prospective interference; the performance of ongoing tasks also improved for all students. Both PM and n-back performances in low WM load were better than in high WM load. High WM load had more influence on low-achieving students than on high-achieving students. Results suggest that low-achieving students in math were weak at PM and influenced more by high WM load. Thus, it is important to train these students to set up an obvious reminder for their PM and improve their WM.

In this paper we present a novel and efficient depth-image representation and warping technique called DMesh which is based on a piece-wise linear approximation of the depth-image as a textured and simplified triangle mesh. We describe the application of a hierarchical multiresolution triangulation method to generate adaptively triangulated depth-meshes efficiently from reference depth-images, discuss depth-mesh segmentation methods to avoid occlusion artifacts and propose a new hardware accelerated depth-image rendering technique that supports per-pixel weighted blending of multiple depth-images in real-time. Applications of our technique include image-based object representations and the use of depth-images in large scale walk-through visualization systems.

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