scholarly journals Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

2020 ◽  
Vol 34 (5) ◽  
pp. 585-594
Author(s):  
Shivangi Anthwal ◽  
Dinesh Ganotra
Keyword(s):  
Interpersonal Communication ◽  
Facial Expressions ◽  
Visual Information ◽  
Human Cognition ◽  
Support Vector ◽  
Dynamic Features ◽  
Difference Image ◽  
Neutral Face ◽  
The Difference ◽  
Emotional Face

Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the degree of correlation of optical flow associated with the image pair and each pre-designed template describing the motion pattern associated with different expressions. The second shape-based descriptor, dyn-HOG, finds the HOG values of the difference image derived by subtracting neutral face from emotional face, and is demonstrated to be more discriminative than previously used static HOG descriptors for classifying facial expressions. Recognition accuracies with multi-class support vector machine obtained on the CK+ and KDEF-dyn datasets are competent with the results of state-of-the-art techniques and empirical analysis of human cognition of emotions.

scholarly journals Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mohammad Rafayet Ali ◽  
Taylor Myers ◽  
Ellen Wagner ◽  
Harshil Ratnu ◽  
E. Ray Dorsey ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Facial Expressions ◽  
Preliminary Evidence ◽  
Support Vector ◽  
Web Based ◽  
Neutral Face ◽  
Automated Classifier ◽  
Objectively Measured ◽  
Prevalent Symptom

AbstractA prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool (www.parktest.net). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.

The Perception of Facial Expressions from Two-Frame Apparent Motion

Perception ◽  
10.1068/p5769 ◽  
2008 ◽  
Vol 37 (10) ◽  
pp. 1560-1568 ◽  
Author(s):  
Naoyuki Matsuzaki ◽  
Takao Sato
Keyword(s):  
Facial Expressions ◽  
Apparent Motion ◽  
Face Image ◽  
Repetition Condition ◽  
Feature Points ◽  
Motion Condition ◽  
Neutral Face ◽  
Minimum Number ◽  
Point Light ◽  
Emotional Face

We examined the contribution of motion information in perceiving facial expressions using point-light displays of faces. First, we established the minimum number of feature points necessary for the perception of facial expression from a single image. Next, we examined the effects of motion with a stimulus using an insufficient number of dots. We used two conditions. In the motion condition, the apparent motion was induced by a preceding neutral face image followed by an emotional face image. In the repetition condition, the same emotional face image was presented twice. The performance was higher in the motion condition than in the repetition condition. This advantage was reduced by inserting a white blank field between the neutral and emotional faces thus confirming that the improvement was due to the motion.

scholarly journals Revealing Traces of Image Resampling and Resampling Antiforensics

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Anjie Peng ◽  
Yadong Wu ◽  
Xiangui Kang
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Ar Model ◽  
Support Vector ◽  
Difference Image ◽  
Art Works ◽  
Vector Machines ◽  
Image Tampering Detection ◽  
The Difference ◽  
High Pass

Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. We find that the interpolation operation used in the resampling and forged resampling makes these two kinds of image show different statistical behaviors from the unaltered images, especially in the high frequency domain. To reveal the traces left by the interpolation, we first apply multidirectional high-pass filters on an image and the residual to create multidirectional differences. Then, the difference is fit into an autoregressive (AR) model. Finally, the AR coefficients and normalized histograms of the difference are extracted as the feature. We assemble the feature extracted from each difference image to construct the comprehensive feature and feed it into support vector machines (SVM) to detect resampling and forged resampling. Experiments on a large image database show that the proposed detector is effective and secure. Compared with the state-of-the-art works, the proposed detector achieved significant improvements in the detection of downsampling or resampling under JPEG compression.

scholarly journals Modelling and Analysis of Facial Expressions Using Optical Flow Derived Divergence and Curl Templates

2021 ◽  
Vol 20 (2) ◽  
pp. 1-21
Author(s):  
Shivangi Anthwal
Keyword(s):  
Optical Flow ◽  
Facial Expressions ◽  
Application Programming Interface ◽  
Human Cognition ◽  
Support Vector ◽  
Potential Applications ◽  
The Face ◽  
Application Programming ◽  
Active Research ◽  
Paralinguistic Communication

Facial expressions are integral part of non-verbal paralinguistic communication as they provide cues significant in perceiving one’s emotional state. Assessment of emotions through expressions is an active research domain in computer vision due to its potential applications in multi-faceted domains. In this work, an approach is presented where facial expressions are modelled and analyzed with dense optical flow derived divergence and curl templates that embody the ideal motion pattern of facial features pertaining to unfolding of an expression on the face. Two types of classification schemes based on multi-class support vector machine and k-nearest neighbour are employed for evaluation. Promising results obtained from comparative analysis of the proposed approach with state-of-the-art techniques on the Extended Cohn Kanade database and with human cognition and pre-trained Microsoft face application programming interface on the Karolinska Directed Emotional Faces database validate the efficiency of the approach.

scholarly journals An Improved Cloud Detection Method for GF-4 Imagery

2020 ◽  
Vol 12 (9) ◽  
pp. 1525
Author(s):  
Ming Lu ◽  
Feng Li ◽  
Bangcheng Zhan ◽  
He Li ◽  
Xue Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Methods ◽  
Support Vector ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Dynamic Features ◽  
Improve Image Quality ◽  
Clear Sky ◽  
Important Means ◽  
The Difference

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.

The Effects of Smoking on Mood, Rapport, and Satisfaction in Conversation among Japanese Smokers in A First Meeting: A Randomized Controlled Trial

2020 ◽  
Author(s):  
Kengo Yokomitsu ◽  
Keita Somatori ◽  
Tomonari Irie
Keyword(s):  
Interpersonal Communication ◽  
Controlled Trial ◽  
Tobacco Consumption ◽  
Cigarette Use ◽  
Intrapersonal Communication ◽  
Face To Face ◽  
Water Group ◽  
Randomized Controlled ◽  
Randomized Control ◽  
The Difference

Through a randomized control design, this study examines whether tobacco consumption contributes to mood change and social enhancement in dyadic conversation. In addition, we would like to focus on the difference between smoking device and cigarette use. Specifically, we compare the intrapersonal and interpersonal communication in dyadic conversation among the cigarette group, the smoking device cigarette group, the water group, and the nothing consumption group. In the present study, 80 participants engaged in a dyadic, face-to-face, unstructured conversation with a stranger of the same gender. Regarding intrapersonal communication, no significant differences were found between the groups’ POMS 2 subscale scores (anger-hostility, confusion-bewilderment, depression-dejection, tension-anxiety, fatigue-inertia, vigour-activity, and friendliness). The smoking device and cigarette groups had greater rapport than the water group. Moreover, the smoking device group were more satisfied with dyadic conversation than the water group. Finally, there were no significant differences in verbal communication among the groups.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1694
Author(s):  
Mathew Ashik ◽  
A. Jyothish ◽  
S. Anandaram ◽  
P. Vinod ◽  
Francesco Mercaldo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Dynamic Features ◽  
System Calls ◽  
Prevention Methods ◽  
Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

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