Facial Expression Recognition in the Wild Using Convolutional Neural Networks

Facial Expression Recognition (FER) is the task of predicting a specific facial expression given a facial image. FER has demonstrated remarkable progress due to the advancement of deep learning. Generally, a FER system as a prediction model is built using two sub-modules: 1. Facial image representation model that learns a mapping from the input 2D facial image to a compact feature representation in the embedding space, and 2. A classifier module that maps the learned features to the label space comprising seven labels of neutral, happy, sad, surprise, anger, fear, or disgust. Ultimately, the prediction model aims to predict one of the seven aforementioned labels for the given input image. This process is carried out using a supervised learning algorithm where the model minimizes an objective function that measures the error between the prediction and true label by searching for the best mapping function. Our work is inspired by Deep Metric Learning (DML) approaches to learn an efficient embedding space for the classifier module. DML fundamentally aims to achieve maximal separation in the embedding space by creating compact and well-separated clusters with the capability of feature discrimination. However, conventional DML methods ignore the underlying challenges associated with wild FER datasets, where images exhibit large intra-class variation and inter-class similarity. First, we tackle the extreme class imbalance that leads to a separation bias toward facial expression classes populated with more data (e.g., happy and neutral) against minority classes (e.g., disgust and fear). To eliminate this bias, we propose a discriminant objective function to optimize the embedding space to enforce inter-class separation of features for both majority and minority classes. Second, we design an adaptive mechanism to selectively discriminate features in the embedding space to promote generalization to yield a prediction model that classifies unseen images more accurately. We are inspired by the human visual attention model described as the perception of the most salient visual cues in the observed scene. Accordingly, our attentive mechanism adaptively selects important features to discriminate in the DML\u27s objective function. We conduct experiments on two popular large-scale wild FER datasets (RAF-DB and AffectNet) to show the enhanced discriminative power of our proposed methods compared with several state-of-the-art FER methods

Performance of Concrete MRF at Near-Field Earthquakes Compared to Far-Field Earthquakes

The characteristic of near-field earthquake records has been investigated in the previous studies. However, the effects of the near-field earthquakes on the response of the building structures need to be further investigated. Engineering demand parameters like inter-story drift ratio and floor acceleration can provide a good means for comparing the response of structures to the near-field and the far-field earthquakes. The main objective of this paper was to apply these two parameters to compare the behavior of the concrete Moment Resistant Frame (MRF) subjected to near-field and far-field ground motions. In this study, non-linear numerical simulations were performed on concrete MRF office buildings subjected to two sets of 14 near-field records and 14 far-field records. The analytical models simulated 4-story, 8-story, and 16 story buildings. The obtained results indicated that the near-field effects can increase the inter-story drift ratio and floor acceleration at lower stories of low and mid-rise building subjected to high ground motion intensities

Diet and non-Hodgkin’s lymphoma risk

Background: The role of dietary factors in the epidemiology of Non-Hodgkin’s lymphoma (NHL) remains largely undefined. Dietary habits may play a role in the etiology of NHL by influencing the immune system. Methods: Dietary patterns and the risk of NHL were analyzed in a case control study; including 170 NHL cases and 190 controls. All subjects completed a validated food-frequency questionnaire. The dietary pattern was investigated separately and in nine nutritional groups. Crosstab tables were used to estimate the odds ratios (OR), and Ptrend. Results: Consumption of highest versus lowest quartile of proteins (OR, 8.088 Ptrend=0.000), fats (OR, 6.17 Ptrend=0.000) and sweets (OR, 8.806 Ptrend=0.000) were associated with a significantly increased NHL risk. The inverse association was found for fresh fruits (OR, 0.117 Ptrend=0.000) and vegetables (OR, 0.461 Ptrend =0.010). Conclusion: An association between dietary intake and the risk of NHL is biologically plausible due to immunosuppressive effects of fat and animal proteins, and antioxidant properties of vegetables and fruits. Pan African Medical Journal 2012; 12:5

A Robust Structured Tracker Using Local Deep Features

Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers

Recognizing Children’s Emotions Using Convolutional Neural Networks

Recognizing emotions via an automated system plays a vital role in a variety of applications such as treating behavipresentation diseases, video surveillance, mood tracking, and human computer interaction. Identifying expressions in adults is challenging, especially during the transition between two emotions. Children have a more complex way of expressing emotions. This makes the recognition task more difficult. In this research, we develop a deep learning approach to detect emotions using visual datasets. We construct a Convolutional Neural Network (CNN) to predict happy and neutral emotions, the two most common expressions among children, after detecting children’s faces in a video. The CNN is a custom VGG13 network consisting of 10 convolution layers interleaved with max pooling and dropout layers. This network is trained on the Facial Expression Recognition (FER+) dataset, an enhanced version of a well-known face dataset with eight annotated emotions for people from different demographics and ages. We explicitly test the proposed approach on a smaller dataset of children’s faces, which are captured from the study of the interaction between children and robots. This test dataset is manually labeled with neutral and happy emotions. The proposed approach achieves an accuracy of 88.79% in predicting two emotions on children’s faces

Evaluation of Depression and its Related Factors Among Female Students in Fasa, Iran

Background: We aimed to determine frequency of depression among female adolescent students and its related factors in Fasa, Iran.Methods: In a cross-sectional study, female high school students were evaluated. Depression, mental disorder and family’s relative peace were measured using standard scales.Results: A total of 516 students were evaluated in which 157 (30.4%) students did not suffer any type of depression. The mean depression score of students had significant relationship with history of an addicted family member (P < 0.001), family relative peace (P < 0.001), history of any mental-psychological disorder in family (P < 0.001) and parents’ educational level (P = 0.03).Conclusion: The prevalence of depression was high in female students and was associated with variables such as drug-addicted family member, relative peace and history of mental-psychological disorders in the family