Micro-expression (ME) analysis has been becoming an attractive topic recently. Nevertheless, the studies of ME mostly focus on the recognition task while spotting task is rarely touched. While micro-expression recognition methods have obtained the promising results by applying deep learning techniques, the performance of the ME spotting task still needs to be largely improved. Most of the approaches still rely upon traditional techniques such as distance measurement between handcrafted features of frames which are not robust enough in detecting ME locations correctly. In this paper, we propose a novel method for ME spotting based on a deep sequence model. Our framework consists of two main steps: 1) From each position of video, we extract a spatial-temporal feature that can discriminate MEs among extrinsic movements. 2) We propose to use a LSTM network that can utilize both local and global correlation of the extracted feature to predict the score of the ME apex frame. The experiments on two publicly databases of ME spotting demonstrate the effectiveness of our proposed method.
Most specular detection methods assumed that dominant highlight regions should be uniform for the detection of highlights, which may not be the case in real images. Even when non-uniformity is allowed in the detection, the specular removal can still suffer from non-converged artifacts due to discontinuities in surface colors, especially in highly textured and multicolor images. In this paper, we propose a novel and effective resolution to separate and remove specular components from a single image by adopting tensor voting to obtain reflectance distribution of an input image. Specular and noise pixels denoted as small tensors are isolated and removed. Diffuse reflectance distribution is achieved by analyzing salient and orientation information of tensors around the specular region. The proposed method is non-iterative and does not require any pre-defined constraints in the input image. We evaluate our proposed method on a dataset consisting of highly textured and multicolor images. Experimental results showed that our result is outstanding compared to other state-of-the-art techniques.
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Micro-expressions (MEs) are brief and involuntary facial expressions when people hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. However, the ME analysis system can still not work well in the real environment because of the challenging performance of ME spotting, which is to spot the images with micro-expressions from long video sequences. To improve the performance of ME spotting, we focus on hybrid feature engineering, which aims to create a robust feature for discriminating tiny movements. The proposed framework consists of two main modules: (1) the feature engineering extracts both geometric features and appearance features based on dynamic image; (2) the new deep neural network inputs the handcrafted feature for the late fusion and ME samples classification. Our experimental results from three baseline datasets demonstrate the promising results.
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