Identification of Facial Expressions using Deep Neural Networks

2020 ◽  
pp. 22-30
Author(s):  
Harsh .. ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Facial Expressions ◽  
Deep Neural Networks ◽  
Good Accuracy ◽  
Data Sets ◽  
Learning Method ◽  
Complex Environments ◽  
Facial Movements ◽  
Modern Methods ◽  
Deep Layers ◽  
The Given

Detecting and analyzing emotions from human facial movements is a problem defined and developed over many years for the benefits it brings. During playback when developing data sets, data sets with methods become more and more complex and accuracy and difficulty increase gradually. In the given paper, we will use a deep structured learned network using the two mechanisms - Vgg and Resnet50 with deep layers to classify emotions based on input images in complex environments, besides that we also use learning methods combining many modern models to increase accuracy. Experimental results show that the two proposed methods have better results than some modern methods in emotional recognition problems for complex input images and some results reported in scientific studies. Particularly combined learning method gives good accuracy - 66.15% on the dataset FER2013

scholarly journals Review of Automatic Microexpression Recognition in the Past Decade

2021 ◽  
Vol 3 (2) ◽  
pp. 414-434
Author(s):  
Liangfei Zhang ◽  
Ognjen Arandjelović
Keyword(s):  
Facial Expressions ◽  
Emotional State ◽  
Small Range ◽  
Regular Expressions ◽  
Data Sets ◽  
Comprehensive Overview ◽  
Psychological States ◽  
Facial Movements ◽  
The Past ◽  
Current State

Facial expressions provide important information concerning one’s emotional state. Unlike regular facial expressions, microexpressions are particular kinds of small quick facial movements, which generally last only 0.05 to 0.2 s. They reflect individuals’ subjective emotions and real psychological states more accurately than regular expressions which can be acted. However, the small range and short duration of facial movements when microexpressions happen make them challenging to recognize both by humans and machines alike. In the past decade, automatic microexpression recognition has attracted the attention of researchers in psychology, computer science, and security, amongst others. In addition, a number of specialized microexpression databases have been collected and made publicly available. The purpose of this article is to provide a comprehensive overview of the current state of the art automatic facial microexpression recognition work. To be specific, the features and learning methods used in automatic microexpression recognition, the existing microexpression data sets, the major outstanding challenges, and possible future development directions are all discussed.

scholarly journals Analysis of Micro Facial Expression by Machine and Deep Learning Methods: Haar, CNN, and RNN

2021 ◽  
Vol 16 (1) ◽  
pp. 95-101
Author(s):  
Dibakar Raj Pant ◽  
Rolisha Sthapit
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Recurrent Neural Network ◽  
Test Accuracy ◽  
Data Sets ◽  
Learning Methods ◽  
Facial Movements ◽  
Casme Ii

Facial expressions are due to the actions of the facial muscles located at different facial regions. These expressions are two types: Macro and Micro expressions. The second one is more important in computer vision. Analysis of micro expressions categorized by disgust, happiness, anger, sadness, surprise, contempt, and fear are challenging because of very fast and subtle facial movements. This article presents one machine learning method: Haar and two deep learning methods: Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) to perform recognition of micro-facial expression analysis. First, Haar Cascade Classifier is used to detect the face as a pre-image-processing step. Secondly, those detected faces are passed through series of Convolutional Neural Network (CNN) layers for the features extraction. Thirdly, the Recurrent Neural Network (RNN) classifies micro facial expressions. Two types of data sets are used for training and testing of the proposed method: Chinese Academy of Sciences Micro-Expression II (CSAME II) and Spontaneous Actions and Micro-Movements (SAMM) database. The test accuracy of SAMM and CASME II are obtained as 84.76%, and 87% respectively. In addition, the distinction between micro facial expressions and non- micro facial expressions are analyzed by the ROC curve.

scholarly journals A novel optimal multi-pattern matching method with wildcards for DNA sequence

2021 ◽  
Vol 29 ◽  
pp. 115-124
Author(s):  
Xinlu Wang ◽  
Ahmed A.F. Saif ◽  
Dayou Liu ◽  
Yungang Zhu ◽  
Jon Atli Benediktsson
Keyword(s):  
Dna Sequence ◽  
Pattern Matching ◽  
Health Informatics ◽  
State Of The Art ◽  
Machine Language ◽  
Data Sets ◽  
Fundamental Issue ◽  
Matching Method ◽  
Dna Sequence Alignment ◽  
The Given

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

Holistic and reductionist thinker : a comparison study based on individuals’ skillset and personality types

2021 ◽  
Author(s):  
Morteza Nagahi ◽  
Raed Jaradat ◽  
Safae El Amrani ◽  
Michael Hamilton ◽  
Simon Goerger
Keyword(s):  
Complex System ◽  
Systems Thinking ◽  
Good Accuracy ◽  
Demographic Characteristics ◽  
Personality Types ◽  
Comparison Study ◽  
Complex Environments ◽  
The Relationship

As organizations operate in turbulent and complex environments, it has become a necessity to assess the systems thinking (ST) skills, personality types (PTs), and demographics of practitioners. In this study, we investigated the relationship between practitioners’ ST profile, their PTs profiles and demographic characteristics in the domain of complex system problems. The objective of this study is to address the current gap in the literature – lack of studies dedicated to predicting practitioners’ ST profile based on their PTs and demographics characteristics. A total of 258 practitioners with different demographics and PTs provided the data. The results show that (1) practitioners can be classified based on their ST skills scores into two clusters: holistic and reductionist (that is, ST profile), (2) each cluster has different PTs profiles and demographic characteristics, and (3) practitioner’s ST profile can be predicted, with good accuracy, based on their PTs profile and demographic characteristics.

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

scholarly journals Does age-dynamic movement accelerate facial age impression? Perception of age from facial movement: Studies of Japanese women

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255570
Author(s):  
Motonori Kurosumi ◽  
Koji Mizukoshi ◽  
Maya Hongo ◽  
Miyuki G. Kamachi
Keyword(s):  
Facial Expressions ◽  
Daily Life ◽  
Japanese Women ◽  
Facial Movement ◽  
Aging Research ◽  
Daily Life Activities ◽  
Facial Movements ◽  
Dynamic Movement ◽  
Age Dependent ◽  
Age Perception

We form impressions of others by observing their constant and dynamically-shifting facial expressions during conversation and other daily life activities. However, conventional aging research has mainly considered the changing characteristics of the skin, such as wrinkles and age-spots, within very limited states of static faces. In order to elucidate the range of aging impressions that we make in daily life, it is necessary to consider the effects of facial movement. This study investigated the effects of facial movement on age impressions. An age perception test using Japanese women as face models was employed to verify the effects of the models’ age-dependent facial movements on age impression in 112 participants (all women, aged 20–49 years) as observers. Further, the observers’ gaze was analyzed to identify the facial areas of interests during age perception. The results showed that cheek movement affects age impressions, and that the impressions increase depending on the model’s age. These findings will facilitate the development of new means of provoking a more youthful impression by approaching anti-aging from a different viewpoint of facial movement.

scholarly journals Mobile Game Education About using Capitals Hiragana and Katakana Fisher-Yates Shuffle Algorithm and Fuzzy Tsukamoto

2021 ◽  
Vol 5 (9) ◽  
pp. 30-34
Author(s):  
Rahmiati* . ◽  
Rika Melyanti ◽  
Suryani Des ◽  
Ambiyar .
Keyword(s):  
Decision Making ◽  
Mobile Devices ◽  
Good Accuracy ◽  
Educational Games ◽  
Vocational School ◽  
Educational Game ◽  
Learning Method ◽  
Mobile Game ◽  
New Learning

Japanese is a different language because it uses the letters Katakana and Hiragana. Japanese learning at the Kansai Vocational School Pekanbaru encountered several obstacles including the lack of learning facilities and a learning atmosphere that tends to be boring so that many students have difficulty learning and choosing to play games. Educational games on mobile devices are a new learning method that is considered to be more attractive to someone to learn. Fisher-Yates is a randomization technique on questions so questions that come out will be different and can be generated without repetition and duplication. Fuzzy Tsukamoto is a method used in decision-making to determine the score at the end of the quiz. From this research, it can be found that Fisher-Yates can determine the randomization solution that is not multiple and varied object randomization. Fuzzy Tsukamoto has a fairly good accuracy between calculations based on the system and calculations manually, although it does not show results that are 100% the same in each calculation. Educational game recognizing the letters Katakana and Hiragana is expected to help students overcome difficulties in understanding and learning Japanese related to the mastery of the letters Katakana and Hiragana.

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