scholarly journals Facial Expression is Retained in Deep Networks Trained for Face Identification

2020 ◽  
Author(s):  
Y. Ivette Colón ◽  
Carlos D. Castillo ◽  
ALICE O'TOOLE
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Visual Cues ◽  
Face Identification ◽  
Two Dimensional ◽  
Multiple Sources ◽  
Deep Convolutional Neural Networks ◽  
Visual Stream ◽  
Linear Discriminant ◽  
Expression Classification

Facial expressions distort visual cues for identification in two-dimensional images. Face processing systems in the brain must decouple image-based information from multiple sources to operate in the social world. Deep convolutional neural networks (DCNN) trained for face identification retain identity-irrelevant, image-based information (e.g., viewpoint). We asked whether a DCNN trained for identity also retains expression information that generalizes over viewpoint change. DCNN representations were generated for a controlled dataset containing images of 70 actors posing 7 facial expressions (happy, sad, angry, surprised, fearful, disgusted, neutral), from 5 viewpoints (frontal, 90-degree and 45-degree left and right profiles). Two-dimensional visualizations of the DCNN representations revealed hierarchical groupings by identity, followed by viewpoint, and then by facial expression. Linear discriminant analysis of full-dimensional representations predicted expressions accurately (72% correct for happiness, followed by surprise, disgust, anger, neutral, sad, and fearful at 39%; chance = 14.29%). Expression classification was stable across viewpoints. Representational similarity heatmaps indicated that image similarities within identities varied more by viewpoint than by expression. We conclude that an identity-trained, deep network retains shape-deformable information about expression and viewpoint, along with identity, in a unified form—consistent with a recent hypothesis for ventral visual stream processing.

scholarly journals Facial Expression Information in Deep Convolutional Neural Networks Trained for Face Identification

2019 ◽  
Vol 19 (10) ◽  
pp. 93b
Author(s):  
Y. Ivette Colon ◽  
Matthew Q Hill ◽  
Connor J Parde ◽  
Carlos D Castillo ◽  
Rajeev Ranjan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Face Identification ◽  
Deep Convolutional Neural Networks

Judging and coding facial expression of emotions in congenitally blind children

2001 ◽  
Vol 25 (3) ◽  
pp. 268-278 ◽  
Author(s):  
Dario Galati ◽  
Renato Miceli ◽  
Barbara Sini
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Control Group ◽  
Two Dimensional ◽  
Coding System ◽  
Facial Movement ◽  
Blind Children ◽  
Congenitally Blind ◽  
Expression Of Emotions ◽  
Facial Expressiveness

We investigate the facial expression of emotions in very young congenitally blind children to ” nd out whether these are objectively and subjectively recognisable. We also try to see whether the adequacy of the facial expression of emotions changes as the children get older. We video recorded the facial expressions of 10 congenitally blind children and 10 sighted children (as a control group) in seven everyday situations considered as emotion elicitors. The recorded sequences were analysed according to the Maximally Discriminative Facial Movement Coding System (Max; Izard, 1979) and then judged by 280 decoders who used four scales (two dimensional and two categorical) for their answers. The results showed that all the subjects (both the blind and the sighted) were able to express their emotions facially, though not always according to the theoretically expected pattern. Recognition of the various expressions was fairly accurate, but some emotions were systematically confused with others. The decoders’ answers to the dimensional and categorical scales were similar for both blind and sighted subjects. Our ” ndings on objective and subjective judgements show that there was no decrease in the facial expressiveness of the blind children in the period of development considered.

scholarly journals A Novel Based 3d Facial Expression Detection Using Recurrent Neural Network

2020 ◽  
pp. 48-53
Author(s):  
Jaswanth K S ◽  
D. Stalin David
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Social Relations ◽  
Recurrent Neural Network ◽  
Breakdown Point ◽  
Early Age ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Highlight Extraction

People periodically have diverse facial expressions and disposition changes in this way. Human facial expression acknowledgment plays a really energetic part in social relations. The acknowledgment of feelings has been an dynamic breakdown point from early age. The real-time location of facial expressions like appall, upbeat, pitiful, irate, anxious, astonish. The proposed framework can recognize 6 diverse facial expression. A facial expression acknowledgment framework needs to perform location and change to 3D image, then the facial highlight extraction, and facial expression classification is worn. Out proposed strategy we should be utilizing Recurrent Neural Network (RNN). This RNN show is prepared on JAFEE and Yale database dataset. This framework has capacity to screen individuals’ feelings, to segregate between feelings and name them fittingly.

scholarly journals Performance comparisons of artificial neural network algorithms in facial expression recognition

2015 ◽  
Vol 4 (4) ◽  
pp. 465 ◽  
Author(s):  
Amira Tayfour Ahmed ◽  
Altahir Mohammed ◽  
Moawia Yahia
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Discriminant Function ◽  
Linear Discriminant Function ◽  
Detection Accuracy ◽  
Back Propagation Algorithm ◽  
Linear Discriminant ◽  
Fisher’S Linear Discriminant ◽  
Performance Comparisons

This paper presents methods for identifying facial expressions. The objective of this paper is to present a combination of texture oriented method with dimensional reduction and use for training the Single-Layer Neural Network (SLN), Back Propagation Algorithm (BPA) and Cerebellar Model Articulation Controller (CMAC) for identifying facial expressions. The proposed methods are called intelligent methods that can accommodate for the variations in the facial expressions and hence prove to be better for untrained facial expressions. Conventional methods have limitations that facial expressions should follow some constraints. To achieve the expression detection accuracy, Gabor wavelet is used in different angles to extract possible textures of the facial expression. The higher dimensions of the extracted texture features are further reduced by using Fisher’s linear discriminant function for increasing the accuracy of the proposed method. Fisher’s linear discriminant function is used for transforming higher-dimensional feature vector into a two-dimensional vector for training proposed algorithms. Different facial emotions considered are angry, disgust, happy, sad, surprise and fear are used. The performance comparisons of the proposed algorithms are presented.

scholarly journals Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks

2021 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
Muhammad Sohail ◽  
Ghulam Ali ◽  
Javed Rashid ◽  
Israr Ahmad ◽  
Sultan H. Almotiri ◽  
...  
Keyword(s):  
Racial Identity ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Large Scale ◽  
Learning Approach ◽  
Expression Recognition ◽  
Facial Structure ◽  
Deep Convolutional Neural Networks ◽  
Joint Learning

Multi-culture facial expression recognition remains challenging due to cross cultural variations in facial expressions representation, caused by facial structure variations and culture specific facial characteristics. In this research, a joint deep learning approach called racial identity aware deep convolution neural network is developed to recognize the multicultural facial expressions. In the proposed model, a pre-trained racial identity network learns the racial features. Then, the racial identity aware network and racial identity network jointly learn the racial identity aware facial expressions. By enforcing the marginal independence of facial expression and racial identity, the proposed joint learning approach is expected to be purer for the expression and be robust to facial structure and culture specific facial characteristics variations. For the reliability of the proposed joint learning technique, extensive experiments were performed with racial identity features and without racial identity features. Moreover, culture wise facial expression recognition was performed to analyze the effect of inter-culture variations in facial expression representation. A large scale multi-culture dataset is developed by combining the four facial expression datasets including JAFFE, TFEID, CK+ and RaFD. It contains facial expression images of Japanese, Taiwanese, American, Caucasian and Moroccan cultures. We achieved 96% accuracy with racial identity features and 93% accuracy without racial identity features.

scholarly journals Classification of facial expressions using SVM and HOG

2020 ◽  
Vol 3 (2) ◽  
pp. 210-215
Author(s):  
Juliansyah Putra Tanjung ◽  
Muhathir Muhathir
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Information Acquisition ◽  
Support Vector ◽  
A Value ◽  
Average Accuracy ◽  
The Face ◽  
Facial Expression Classification ◽  
Expression Classification

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.

scholarly journals The Analysis of Emotion Authenticity Based on Facial Micromovements

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4616
Author(s):  
Sung Park ◽  
Seong Won Lee ◽  
Mincheol Whang
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Future Research ◽  
Monitoring Systems ◽  
Two Dimensional ◽  
Expression Data ◽  
Vibration Level ◽  
Emotion Model ◽  
Recognition Systems

People tend to display fake expressions to conceal their true feelings. False expressions are observable by facial micromovements that occur for less than a second. Systems designed to recognize facial expressions (e.g., social robots, recognition systems for the blind, monitoring systems for drivers) may better understand the user’s intent by identifying the authenticity of the expression. The present study investigated the characteristics of real and fake facial expressions of representative emotions (happiness, contentment, anger, and sadness) in a two-dimensional emotion model. Participants viewed a series of visual stimuli designed to induce real or fake emotions and were signaled to produce a facial expression at a set time. From the participant’s expression data, feature variables (i.e., the degree and variance of movement, and vibration level) involving the facial micromovements at the onset of the expression were analyzed. The results indicated significant differences in the feature variables between the real and fake expression conditions. The differences varied according to facial regions as a function of emotions. This study provides appraisal criteria for identifying the authenticity of facial expressions that are applicable to future research and the design of emotion recognition systems.

The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

2020 ◽  
Author(s):  
Jonathan Yi ◽  
Philip Pärnamets ◽  
Andreas Olsson
Keyword(s):  
Decision Making ◽  
Facial Expression ◽  
Facial Expressions ◽  
Large Body ◽  
Mechanistic Explanation ◽  
Experimental Manipulation ◽  
Corrugator Supercilii ◽  
Dynamic Faces ◽  
Facial Behavior ◽  
Reinforcement Learning Models

Responding appropriately to others’ facial expressions is key to successful social functioning. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how goal directed responses to faces are influenced by learning during dyadic interactions. To experimentally model such situations, we developed a novel method based on online integration of electromyography (EMG) signals from the participants’ face (corrugator supercilii and zygomaticus major) during facial expression exchange with dynamic faces displaying happy and angry facial expressions. Fifty-eight participants learned by trial-and-error to avoid receiving aversive stimulation by either reciprocate (congruently) or respond opposite (incongruently) to the expression of the target face. Our results validated our method, showing that participants learned to optimize their facial behavior, and replicated earlier findings of faster and more accurate responses in congruent vs. incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, as compared to frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Finally, we applied drift diffusion and reinforcement learning models to provide a mechanistic explanation for our findings which helped clarifying the underlying decision-making processes of our experimental manipulation. Our results introduce a new method to study learning and decision-making in facial expression exchange, in which there is a need to gradually adapt facial expression selection to both social and non-social reinforcements.

Capacity-Free Automatic Processing of Facial Expressions of Emotion

2020 ◽  
Author(s):  
Joshua W Maxwell ◽  
Eric Ruthruff ◽  
michael joseph
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Processing ◽  
Emotional Expressions ◽  
Facial Expression Of Emotion ◽  
Expression Of Emotion ◽  
Facial Expressions Of Emotion ◽  
Correspondence Effect ◽  
Experimental Logic ◽  
Face Task

Are facial expressions of emotion processed automatically? Some authors have not found this to be the case (Tomasik et al., 2009). Here we revisited the question with a novel experimental logic – the backward correspondence effect (BCE). In three dual-task studies, participants first categorized a sound (Task 1) and then indicated the location of a target face (Task 2). In Experiment 1, Task 2 required participants to search for one facial expression of emotion (angry or happy). We observed positive BCEs, indicating that facial expressions of emotion bypassed the central attentional bottleneck and thus were processed in a capacity-free, automatic manner. In Experiment 2, we replicated this effect but found that morphed emotional expressions (which were used by Tomasik) were not processed automatically. In Experiment 3, we observed similar BCEs for another type of face processing previously shown to be capacity-free – identification of familiar faces (Jung et al., 2013). We conclude that facial expressions of emotion are identified automatically when sufficiently unambiguous.

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