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Recently, there are increasing interests in inferring mirco-expression from facial image sequences. For micro-expression recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-expressions with subtle facial movement. Firstly, an integral projection method based on difference images...
This paper presents large-scale naturalistic and spontaneous facial expression classification on uncontrolled webcam data. We describe an active learning approach that helped us efficiently acquire and hand-label hundreds of thousands of non-neutral spontaneous and natural expressions from thousands of different individuals. With the increased numbers of training samples a classic RBF SVM classifier,...
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn. In this work, not only do we show that CNNs can achieve strong performance, but we also introduce an approach to...
We present an open source cross platform technology for 3D face tracking and analysis. It contains a full stack of components for complete face understanding: detection, head pose tracking, facial expression and action units recognition. Given a depth sensor, one can combine FaceCept3D modules to fulfill a specific application scenario. Key advantages of the technology include real time processing...
Metamer mismatching (the phenomena that two objects matching in color under one illuminant may not match under a different illuminant) potentially has important consequences for color-based machine vision. Logvinenko et al. [1] show that in theory the extent of metamer mismatching can be very significant. This paper examines metamer mismatching in practice by computing empirical metamer mismatch volumes...
Preserving the high dynamic irradiance of a scene is essential for many computer vision algorithms. In this paper, we develop a technique for high dynamic range (HDR) reconstruction from differently exposed frames captured with CMOS cameras which use a rolling shutter (RS) to good effect for reducing power consumption. However, because these sensors are exposed to the scene row-wise, any unintentional...
Illumination estimation is a well-studied topic in computer vision. Early work reported performance on benchmark datasets using simple statistical aggregates such as mean or median error. Recently, it has become accepted to report a wider range of statistics, e.g. top 25%, mean, and bottom 25% performance. While these additional statistics are more informative, their relationship across different...
Commercial Light-Field cameras provide spatial and angular information, but its limited resolution becomes an important problem in practical use. In this paper, we present a novel method for Light-Field image super-resolution (SR) via a deep convolutional neural network. Rather than the conventional optimization framework, we adopt a datadriven learning method to simultaneously up-sample the angular...
Visualization of a multispectral image in a standard color space, typically the sRGB space, is an important task for human color perception. When we reproduce the sRGB image from the multispectral image with N spectral bands, an N-to-sRGB mapping is required. The challenge of the N-to-sRGB mapping in single-sensor multispectral imaging with a multispectral filter array (MSFA) is to reduce demosaicking...
The cross-depiction problem is that of recognising visual objects regardless of whether they are photographed, painted, drawn, etc. It introduces great challenge as the variance across photo and art domains is much larger than either alone. We extensively evaluate classification, domain adaptation and detection benchmarks for leading techniques, demonstrating that none perform consistently well given...
We address the problem of domain adaptation (DA) from one or multiple source domains to a target domain. Most of the existing DA methods assume that source data is largely available. Such an assumption rarely holds in real applications, for both technical and legal reasons. More realistic are situations where source domain observations become quickly unavailable, but only some domain representatives...
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