Emotion recognition based on facial expressions is very important for effective interaction of humans with artificial intelligence (AI) systems such as social robots. On the other hand, in real environment, it is much harder to recognize facial micro-expressions (FMEs) than facial general-expressions having rich emotions. In this paper, we propose a two-dimensional (2D) landmark feature map for effectively recognizing such FMEs. The proposed 2D landmark feature map (LFM) is obtained by transforming conventional coordinate-based landmark information into 2D image information. LFM is designed to have an advantageous property independent of the intensity of facial expression change. Also, we propose an LFM-based emotion recognition method that is an integrated framework of convolutional neural network (CNN) and long shortterm memory (LSTM). Experimental results show that the proposed method achieves about 71% and 74% in the well-known micro-expression datasets, i.e., SMIC and CASME II, respectively, which outperforms the conventional methods. The performance of the proposed method was also verified through experiments on composite micro-expression dataset, which consists of SMIC, CAMSE II and SAMM, and cross-dataset validation using SMIC and CAMSE II. In addition, we prove that the proposed method is independent of facial expression intensity through an experiment on CK+ dataset. Finally, we demonstrate that the proposed method is valid even for the MAHNOB-HCI and MEVIEW datasets that are produced to monitor actual and wild emotional responses.
The leachate generated by the decomposition of animal carcass has been implicated as an environmental contaminant surrounding the burial site. High-throughput nucleotide sequencing was conducted to investigate the bacterial communities in leachates from the decomposition of pig carcasses. We acquired 51,230 reads from six different samples (1, 2, 3, 4, 6 and 14 week-old carcasses) and found that sequences representing the phylum Firmicutes predominated. The diversity of bacterial 16S rRNA gene sequences in the leachate was the highest at 6 weeks, in contrast to those at 2 and 14 weeks. The relative abundance of Firmicutes was reduced, while the proportion of Bacteroidetes and Proteobacteria increased from 3–6 weeks. The representation of phyla was restored after 14 weeks. However, the community structures between the samples taken at 1–2 and 14 weeks differed at the bacterial classification level. The trend in pH was similar to the changes seen in bacterial communities, indicating that the pH of the leachate could be related to the shift in the microbial community. The results indicate that the composition of bacterial communities in leachates of decomposing pig carcasses shifted continuously during the study period and might be influenced by the burial site.
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