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In recent years, large-scale person re-identification has attracted a lot of attention from video surveillance. Usual approaches addressing this task either learn the effective feature embeddings or design the learning architectures to obtain discriminative metrics. Most of them only focus on improving the accuracy of recognition but neglect retrieval efficiency. To improve the accuracy and efficiency of person re-identification simultaneously, an accurate and fast method is proposed based on the bag of visual words (BoVW) model, which has widely been applied in image retrieval. A bag of local features is developed to simplify feature representation for person re-identification. Cross-view dictionary learning is used to eliminate the redundancy of these local features. These local features consist of scale invariant feature transform and local maximal occurrence representation (LOMO) that are invariant in scale and color, respectively. Finally, integrated BoVW histograms are obtained, which encode the images by k-means clustering. Experiments conducted on the CUHK03, Market1501, and MARS datasets show that the proposed method performs favorably against existing approaches. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.5.053041] [ABSTRACT FROM AUTHOR]

Hallucinating a photo-realistic frontal face image from a low-resolution (LR) non-frontal face image is beneficial for a series of face-related applications. However, previous efforts either focus on super-resolving high-resolution (HR) face images from nearly frontal LR counterparts or frontalizing non-frontal HR faces. It is necessary to address all these challenges jointly for real-world face images in unconstrained environment. In this paper, we develop a novel Cross-view Information Interaction and Feedback Network (CVIFNet), which simultaneously handles the non-frontal LR face image super-resolution (SR) and frontalization in a unified framework and interacts them with each other to further improve their performance. Specifically, the CVIFNet is composed of two feedback sub-networks for frontal and profile face images. Considering the reliable correspondence between frontal and non-frontal face images can be crucial and contribute to face hallucination in a different manner, we design a cross-view information interaction module (CVIM) to aggregate HR representations of different views produced by the SR and frontalization processes to generate finer face hallucination results. Besides, since 3D rendered facial priors contain rich hierarchical features, such as low-level (e.g., sharp edge and illumination) and perception level (e.g., identity) information, we design an identity-preserving consistency loss based on 3D rendered facial priors, which can ensure that the high-frequency details of frontal face hallucination result are consistent with the profile. Extensive experiments demonstrate the effectiveness and advancement of CVIFNet. • A novel unified framework, named Cross-view Information Interaction and Feedback Network, is proposed to jointly tackle face SR and face frontalization problems. • A novel feedback mechanism is proposed to provide the high-level reconstruction information in top-down feedback flows through feedback connections. • A novel 3D rendered facial priors based identity-preserving consistency loss is proposed to supervise the reconstruction of facial components spatial information. [ABSTRACT FROM AUTHOR]