Two-Stage Recognition and beyond for Compound Facial Emotion Recognition

Abstract

Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

Ensemble Approach for Detection of Depression Using EEG Features

“…One of the most widely used methods to analyse EEG signals is to decompose the signal into functionally distinct frequency bands, such as delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). In the current study, this was achieved by first calculating the power spectral density of the EEG signal by Welch's method, as done by Bachmann et al 2018.…”

“…Hence, detecting mental states and disorders by using various EEG feature representations, such as methods based on fast Fourier transform (FFT), discrete wavelet transform (DWT), power spectral analysis (PSA), and others [ 8 , 9 , 10 , 11 , 12 , 13 ], is an actively researched field showing promising results. Various advanced machine learning algorithms have been utilised in order to analyse different modalities of such data in order to introduce automated assessment of depression [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

“…Automated detection methods have been developed to address the issues with the huge amount of data and manual analysis. Deep-learningbased methods hold great importance in detection tasks and perform successfully in various domains [12,[16][17][18]. The proven performance of deep learning techniques directed researchers to study deep neural networks (DNNs) in respect to the animal detection task.…”

“…A drawback of using camera traps can be the accumulation of large amounts of images or videos that have to be manually sorted and classified [8,9]. Machine learning models have been used to alleviate this task and it has been shown that it can perform as good as or, in some cases, even better than human-made classifications [9][10][11][12][13]. Recently, computer-vision-and deeplearning-based methods have become popular in camera trapping to automatically identify animal species, counts, behaviors and demographic compositions.…”

Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset

“…The state-of-the-art research show that one of the most successful ways of obtaining texture features from the images is using CNN-based deep learning methods [15,[21][22][23]. Thanks to their convolutional structure, these methods process each pixel and their relation with neighbouring pixels together.…”