MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild

Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accurately imitate human emotions and fulfill applications in the real world. In this paper, we propose MAFW, a large-scale multi-modal compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment. To ensure high quality of the labels, we filter out the unreliable annotations by an Expectation Maximization (EM) algorithm, and then obtain 11 single-label emotion categories and 32 multi-label emotion categories. To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions. Additionally, we also propose a novel Transformer-based expression snippet feature learning method to recognize the compound emotions leveraging the expression-change relations among different emotions and modalities. Extensive experiments on MAFW database show the advantages of the proposed method over other state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database is publicly available from https://mafw-database.github.io/MAFW.Comment: This paper has been accepted by ACM MM'2

Knowledge, Attitudes and Practices of Sanitation and Hygiene among Primary School Students in Rural Area of Northeast China

Disease burden due to unsafe water, lack of sanitation, and poor hygiene behavior requires attention. In developing countries, poor school hygiene behavior remains high-risk and causes infectious disease among students. Safe hygiene behavior such as hand washing with soap can protect children from infectious disease. However, a cross-sectional study found the correct rate of hand washing of Chinese people was only 4%. Our research evaluated the knowledge, attitudes, and practices (KAP) of sanitation and hygiene among school children in the rural area of Northeast China. Participants were 333 groups of students and their parents. A questionnaire was used in the participants who reported the score of KAP level of sanitation and hygiene. Hand washing skill was checked following a checklist. Observation of sanitation facilities at school was also conducted. The questionnaires included participant characteristic, household socioeconomic status, and KAP questionnaire. The results of the questionnaires survey showed more than 80% of students had adequate knowledge of proper hygiene. Although students have sufficient knowledge about hygiene, lack of facilities may negatively affect their practice. There was no soap available in 2 schools, 53% of students reported it affects their hand washing performance at school. The results indicated the impact of gender, facilities and knowledge level on behavior. Our findings underscore the need for more hygiene education and the improvement of sanitation and hygiene facilities in the area