Automatic Hidden Sadness Detection Using Micro-Expressions

Grobova, Jelena; Prodanović, Marijana; Marjanović, Marina; Njeguš, Angelina; Demire, Hasan; Anbarjafari, Gholamreza

doi:10.1109/fg.2017.105

Cited by 13 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A set of experiments is conducted for identical twins based on their face images by using 352 users (176 identical twins) and 1512 image samples from ND-TWINS-2009-2010 Dataset. Four algorithms, namely convolutional neural networks (CNN) [33][34][35], PCA, HOG, and LBP are implemented for comparison purposes. Additionally, three fusion methods namely feature-level, score-level, and decision-level fusion and the proposed method are implemented in order to find the most reliable system that is able to correctly match identical twins by face recognition.…”

Section: Methodsmentioning

confidence: 99%

Recognition of identical twins using fusion of various facial feature extractors

Afaneh

Noroozi

Toygar

2017

J Image Video Proc.

View full text Add to dashboard Cite

Distinguishing identical twins using their face images is a challenge in biometrics. The goal of this study is to construct a biometric system that is able to give the correct matching decision for the recognition of identical twins. We propose a method that uses feature-level fusion, score-level fusion, and decision-level fusion with principal component analysis, histogram of oriented gradients, and local binary patterns feature extractors. In the experiments, face images of identical twins from ND-TWINS-2009-2010 database were used. The results show that the proposed method is better than the state-of-the-art methods for distinguishing identical twins. Variations in illumination, expression, gender, and age of identical twins' faces were also considered in this study. The experimental results of all variation cases demonstrated that the most effective method to distinguish identical twins is the proposed method compared to the other approaches implemented in this study. The lowest equal error rates of identical twins recognition that are achieved using the proposed method are 2.07% for natural expression, 0.0% for smiling expression, and 2.2% for controlled illumination compared to 4.5, 4.2, and 4.7% equal error rates of the best state-of-the-art algorithm under the same conditions. Additionally, the proposed method is compared with the other methods for non-twins using the same database and standard FERET subsets. The results achieved by the proposed method for non-twins identification are also better than all the other methods under expression, illumination, and aging variations.

show abstract

Section: Methodsmentioning

confidence: 99%

Recognition of identical twins using fusion of various facial feature extractors

Afaneh

Noroozi

Toygar

2017

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Li et al [19] used deep multi-task CNNs to locate key points on the face to better extract the optical flow features of the ME frame sequence for ME detection tasks. Grobava et al [20] proposed a novel method, which uses facial landmarks as input vectors and classify the MEs by SVM and random forest classifiers. Mayya et al [21] have performed temporal interpolation on the original video sequence and proposed the Deep Convolution Neural Network (DCNN) framework to extract features from the images.…”

Section: Related Workmentioning

confidence: 99%

“…Grobava et al . [20] proposed a novel method, which uses facial landmarks as input vectors and classify the MEs by SVM and random forest classifiers. Mayya et al .…”

Section: Related Workmentioning

confidence: 99%

TSNN: Three‐Stream Combining 2D and 3D Convolutional Neural Network for Micro‐Expression Recognition

Guo

2020

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Non-member Fan Guo a , Non-member Facial micro-expression recognition is a natural mechanism of facial behavior with subtle muscle movements and short duration, which is widely considered to be hard to recognize. In this paper, we propose the temporal sampling deformation (TSD) to normalize the temporal lengths and conserve time domain information for micro-expression sequences. A three-stream combining 2D and 3D convolutional neural network (TSNN) is also proposed to capture the features of micro-expressions and classify the expressions as well. The proposed network has two variants TSNN-IF and TSNN-LF, which can automatically learn spatial and temporal features at the same time. Single domain experiments and cross-domain experiments are also performed in the three benchmark datasets (chinese academy of sciences micro-expression II (CASME II), spontaneous micro-expression database (SMIC), and spontaneous micro-facial movement dataset (SAMM)) to verify the effectiveness and validity of the proposed framework. Comprehensive results and ablation studies show that the proposed method can achieve comparable or even better results compared with other state-of-the-art methods for micro-expression recognition.

show abstract

“…Deep neural networks, due to their high accuracy, are widely used in many of the computer vision applications such as emotion recognition [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], biometric recognition [ 17 , 18 , 19 , 20 ], personality analysis [ 21 , 22 ], and activity analysis [ 5 , 23 , 24 ]. Depending on the nature of the data, different structures can be used [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Action Recognition Using Single-Pixel Time-of-Flight Detection

Ofodile

Helmi

Clapés

et al. 2019

Entropy

Self Cite

View full text Add to dashboard Cite

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.

show abstract

Automatic Hidden Sadness Detection Using Micro-Expressions

Cited by 13 publications

References 25 publications

Recognition of identical twins using fusion of various facial feature extractors

Recognition of identical twins using fusion of various facial feature extractors

TSNN: Three‐Stream Combining 2D and 3D Convolutional Neural Network for Micro‐Expression Recognition

Action Recognition Using Single-Pixel Time-of-Flight Detection

Contact Info

Product

Resources

About