SSIM Based Signature of Facial Micro-Expressions

Bruni, Vittoria; Vitulano, Domenico

doi:10.1007/978-3-030-50347-5_24

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Another class of methods focuses on specific temporal phases by looking for, for example, the apex frame through the characterization of geometric and/or appearance and/or saliency features of specific facial components [ 19 , 20 , 21 , 22 , 23 ]. A first attempt to define a perceptual fingerprint of ME has been done in [ 24 ] by looking at an ME as a perceptual discontinuity. Even though preliminary results are promising, the use of high-pass details suffers from some sensitivity to noise or local movements that can provide some false alarms, including eye blinking.…”

Section: Motivation Of the Workmentioning

confidence: 99%

A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

Bruni

Vitulano

2021

J. Imaging

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This paper presents a preliminary study concerning a fast preprocessing method for facial microexpression (ME) spotting in video sequences. The rationale is to detect frames containing frozen expressions as a quick warning for the presence of MEs. In fact, those frames can either precede or follow (or both) MEs according to ME type and the subject’s reaction. To that end, inspired by the Adelson–Bergen motion energy model and the instinctive nature of the preattentive vision, global visual perception-based features were employed for the detection of frozen frames. Preliminary results achieved on both controlled and uncontrolled videos confirmed that the proposed method is able to correctly detect frozen frames and those revealing the presence of nearby MEs—independently of ME kind and facial region. This property can then contribute to speeding up and simplifying the ME spotting process, especially during long video acquisitions.

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Section: Motivation Of the Workmentioning

confidence: 99%

A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

Bruni

Vitulano

2021

J. Imaging

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“…Many measures of skewness have been developed since the late 19th century, but one of the first measurement proposals, made by Karl Pearson [2], still constitutes one of the best choices: the standardized signed distance from the arithmetic mean to the median [3] [4] [5] [6]. Its interpretation is intuitive and clear, especially in comparison with the unimodal profile of a normal distribution.…”

Section: Introductionmentioning

confidence: 99%

Standardized Distance from the Mean to the Median as a Measure of Skewness

Rubia¹

2023

OJS

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The normal distribution, which has a symmetric and middle-tailed profile, is one of the most important distributions in probability theory, parametric inference, and description of quantitative variables. However, there are many non-normal distributions and knowledge of a non-zero bias allows their identification and decision making regarding the use of techniques and corrections. Pearson's skewness coefficient defined as the standardized signed distance from the arithmetic mean to the median is very simple to calculate and clear to interpret from the normal distribution model, making it an excellent measure to evaluate this assumption, complemented with the visual inspection by means of a histogram and a box-and-whisker plot. From its variant without tripling the numerator or Yule's skewness coefficient, the objective of this methodological article is to facilitate the use of this latter measure, presenting how to obtain asymptotic and bootstrap confidence intervals for its interpretation. Not only are the formulas shown, but they are applied with an example using R program. A general rule of interpretation of ∓0.1 has been suggested, but this can only become relevant when contextualized in relation to sample size and a measure of skewness with a population or parametric value of zero. For this purpose, intervals with confidence levels of 90%, 95% and 99% were estimated with 10,000 draws at random with replacement from 57 normally distributed samples-population with different sample sizes. The article closes with suggestions for the use of this measure of skewness.

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Anomaly detection of complex magnetic measurements using structured Hankel low-rank modeling and singular value decomposition

Zhang

Liu

Wang

et al. 2022

Review of Scientific Instruments

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The magnetic anomalies generated by the ferromagnetic targets are usually buried within uncontrollable interference sources, such as the power frequency and random noises. In particular, the variability of the geomagnetic field and the low signal-to-noise ratio (SNR) of the magnetic anomalies cannot be avoided. In this paper, to improve the performance of magnetic anomaly detection (MAD) with a low SNR, we propose a novel structured low-rank (SLR) decomposition-based MAD method. In addition, a new framework based on the SLR and singular value decomposition (SVD) is constructed, dubbed SLR-SVD, and the corresponding working principle and implemented strategy are elaborated. Through comparing the SLR-SVD with two state-of-the-art methods, including principal component analysis and SVD, the results demonstrate that the proposed SLR-SVD can not only suppress the noise sufficiently, i.e., improving 55.26% approximately of the SNR, but also retain more boundary information of magnetic anomalies, i.e., decreasing approximately 68.05% of the mean squared error and improving approximately 28.47% of the structural similarity index.

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SSIM Based Signature of Facial Micro-Expressions

Cited by 5 publications

References 17 publications

A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

Standardized Distance from the Mean to the Median as a Measure of Skewness

Anomaly detection of complex magnetic measurements using structured Hankel low-rank modeling and singular value decomposition

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