scholarly journals The Effects of Facial Expressions on Face Biometric System’s Reliability

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 485
Author(s):  
Hind A. Alrubaish ◽  
Rachid Zagrouba
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Rejection Rate ◽  
Facial Features ◽  
Biometric System ◽  
Acceptance Threshold ◽  
Face Shape ◽  
Biometric Systems ◽  
The Face ◽  
Face Biometric

The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.

Effects of Briefly Presented Masked Emotional Facial Expressions on Gaze Behavior: An Eye-Tracking Study

2018 ◽  
Vol 122 (4) ◽  
pp. 1432-1448 ◽  
Author(s):  
Charlott Maria Bodenschatz ◽  
Anette Kersting ◽  
Thomas Suslow
Keyword(s):  
Eye Movements ◽  
Facial Expression ◽  
Eye Tracking ◽  
Facial Expressions ◽  
Affective Priming ◽  
Facial Features ◽  
Emotional Facial Expressions ◽  
Mouth Region ◽  
The Face ◽  
Eye Region

Orientation of gaze toward specific regions of the face such as the eyes or the mouth helps to correctly identify the underlying emotion. The present eye-tracking study investigates whether facial features diagnostic of specific emotional facial expressions are processed preferentially, even when presented outside of subjective awareness. Eye movements of 73 healthy individuals were recorded while completing an affective priming task. Primes (pictures of happy, neutral, sad, angry, and fearful facial expressions) were presented for 50 ms with forward and backward masking. Participants had to evaluate subsequently presented neutral faces. Results of an awareness check indicated that participants were subjectively unaware of the emotional primes. No affective priming effects were observed but briefly presented emotional facial expressions elicited early eye movements toward diagnostic regions of the face. Participants oriented their gaze more rapidly to the eye region of the neutral mask after a fearful facial expression. After a happy facial expression, participants oriented their gaze more rapidly to the mouth region of the neutral mask. Moreover, participants dwelled longest on the eye region after a fearful facial expression, and the dwell time on the mouth region was longest for happy facial expressions. Our findings support the idea that briefly presented fearful and happy facial expressions trigger an automatic mechanism that is sensitive to the distribution of relevant facial features and facilitates the orientation of gaze toward them.

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

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.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals Transmitting and Decoding Facial Expressions

2005 ◽  
Vol 16 (3) ◽  
pp. 184-189 ◽  
Author(s):  
Marie L. Smith ◽  
Garrison W. Cottrell ◽  
FrédéAric Gosselin ◽  
Philippe G. Schyns
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Brain Structures ◽  
Human Face ◽  
The Face ◽  
The Brain

This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

Pain Assessment through Facial Expression

2016 ◽  
pp. 59-80
Author(s):  
Sanjay Kumar Singh ◽  
V. Rastogi ◽  
S. K. Singh
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Pain Assessment ◽  
Medical Psychology ◽  
Important Symptom ◽  
The Face ◽  
Proper Diagnosis ◽  
Heath Care ◽  
Visual Changes

Pain, assumed to be the fifth vital sign, is an important symptom that needs to be adequately assessed in heath care. The visual changes reflected on the face of a person in pain may be apparent for only a few seconds and occur instinctively. Tracking these changes is a difficult and time-consuming process in a clinical setting. This is why it is motivating researchers and experts from medical, psychology and computer fields to conduct inter-disciplinary research in capturing facial expressions. This chapter contains a comprehensive review of technologies in the study of facial expression along with its application in pain assessment. The facial expressions of pain in children's (0-2 years) and in non-communicative patients need to be recognized as they are of utmost importance for proper diagnosis. Well designed computerized methodologies would streamline the process of patient assessment, increasing its accessibility to physicians and improving quality of care.

The Auditory Kuleshov Effect: Multisensory Integration in Movie Editing

Perception ◽  
2016 ◽  
Vol 46 (5) ◽  
pp. 624-631 ◽  
Author(s):  
Andreas M. Baranowski ◽  
H. Hecht
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Multisensory Integration ◽  
Interaction Effect ◽  
Neutral Face ◽  
The Face ◽  
Sad Music ◽  
Ambiguous Situations

Almost a hundred years ago, the Russian filmmaker Lev Kuleshov conducted his now famous editing experiment in which different objects were added to a given film scene featuring a neutral face. It is said that the audience interpreted the unchanged facial expression as a function of the added object (e.g., an added soup made the face express hunger). This interaction effect has been dubbed “Kuleshov effect.” In the current study, we explored the role of sound in the evaluation of facial expressions in films. Thirty participants watched different clips of faces that were intercut with neutral scenes, featuring either happy music, sad music, or no music at all. This was crossed with the facial expressions of happy, sad, or neutral. We found that the music significantly influenced participants’ emotional judgments of facial expression. Thus, the intersensory effects of music are more specific than previously thought. They alter the evaluation of film scenes and can give meaning to ambiguous situations.

Face aftereffect in haptic perception

2012 ◽  
Vol 25 (0) ◽  
pp. 46-47
Author(s):  
Kazumichi Matsumiya
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Haptic Perception ◽  
Three Dimensional ◽  
Eyes Closed ◽  
Neutral Face ◽  
Visual Phenomenon ◽  
The Face ◽  
The Right ◽  
Graphics Software

Adaptation to a face belonging to a facial category, such as expression, causes a subsequently neutral face to be perceived as belonging to an opposite facial category. This is referred to as the face aftereffect (FAE) (Leopold et al., 2001; Rhodes et al., 2004; Webster et al., 2004). The FAE is generally thought of as being a visual phenomenon. However, recent studies have shown that humans can haptically recognize a face (Kilgour and Lederman, 2002; Lederman et al., 2007). Here, I investigated whether FAEs could occur in haptic perception of faces. Three types of facial expressions (happy, sad and neutral) were generated using a computer-graphics software, and three-dimensional masks of these faces were made from epoxy-cured resin for use in the experiments. An adaptation facemask was positioned on the left side of a table in front of the participant, and a test facemask was placed on the right. During adaptation, participants haptically explored the adaptation facemask with their eyes closed for 20 s, after which they haptically explored the test facemask for 5 s. Participants were then requested to classify the test facemask as either happy or sad. The experiment was performed under two adaptation conditions: (1) with adaptation to a happy facemask and (2) with adaptation to a sad facemask. In both cases, the expression of the test facemask was neutral. The results indicate that adaptation to a haptic face that belongs to a specific facial expression causes a subsequently touched neutral face to be perceived as having the opposite facial expression, suggesting that FAEs can be observed in haptic perception of faces.

Facial Expression in Primates With Remarks On a Parallel Development in Certain Carnivores (a Preliminary Report On Work in Progress)

Behaviour ◽  
1964 ◽  
Vol 22 (3-4) ◽  
pp. 167-192 ◽  
Author(s):  
Niels Bolwig
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Preliminary Report ◽  
Common Ancestry ◽  
Upper Lip ◽  
Common Pattern ◽  
Facial Muscles ◽  
Facial Musculature ◽  
The Face ◽  
Emotional Condition

AbstractIn this report of an unfinished study of the evolution of facial expressions the author draws a brief comparison between the most important facial muscles of various primates and of two carnivores, the suricate and the dog. Before discussing the expressions, definitions of the various elementary emotions are given and the criteria from which the author judges the emotional condition of the animals. The main conclusions reached from the observations are:- 1. Certain basic rules govern the facial expressions of the animals studied. 2. Joy and happiness are expressed by a general lifting of the face and a tightening of the upper lip. The expression originates from preparation for a play-bite. The posture has become completely ritualised in man. 3. Unhappiness expresses itself by a lowering of the face. In horror there is a general tension of the facial muscles and the mouth tends to open while the animal screams. In sadness the animal tends to become less active. 4. Anger is recognisable from a tightening of the facial muscles, particularly those around the mouth in preparation for a hard bite. 5. Threat varies in expression but it contains components of anger and fear. 6. Love and affection find expression through such actions as lipsmacking, love-biting, sucking and kissing. The oral caressing has its origin in the juvenile sucking for comfort. 7. Concentration is not an emotion but it usually shows itself by a tension of the facial muscles. 8. There is a similarity between the two carnivores under discussion and some of the primates. A common pattern of the facial muscles of the suricate and the lemur indicate a common ancestry and brings the two animals to the same level in their ability to express their emotions. The dog, although very different from the monkey in its facial musculature nevertheless resembles it in its mode of expression. This feature seems related to similarities in their biology which have been facilitated by the development of a bifocal vision.

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