Perspectives on Human Variation through the Lens of Diversity and Race: Figure 1.

Chakravarti, Aravinda

doi:10.1101/cshperspect.a023358

Cited by 23 publications

(17 citation statements)

References 39 publications

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“…An image that is seen to have a particular emotion by one person might be seen to have a different emotion by another person. This is due to the different perspective that we all have, because of neurobiological processes (Chakravarti, 2015) i.e. due to the way our brain process information and mediate actions.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Different Coverings

Shehu¹,

Wn²,

Eisenbarth³

2021

Preprint

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Partial face coverings such as sunglasses and facemasks have now become the ‘new norm’, especially since the increase of infectious diseases. Unintentionally, they obscure facial expressions. Therefore, humans and artificial systems have been found to be less accurate in emotion categorization. However, it is unknown how similar the performance of humans compared with artificial systems is affected based on the exact same stimuli, varying systematically in types of coverings. Such a systematic direct comparison would allow conclusions about the relevant facial features in a naturalistic context. Therefore, we investigated the impact of facemasks and sunglasses on the ability to categorize emotional facial expressions in humans and artificial systems. Artificial systems, represented by the VGG19 deep learning algorithm, and humans assessed images of people with varying emotional facial expressions and with four different types of coverings, i.e. unmasked (original images), mask (mask covering lower-face), partial mask (with transparent mouth window), and sunglasses. Artificial systems performed significantly better than humans when no covering is present (> 15% difference). However, the achieved accuracy of both humans and artificial systems differed significantly depending on the type of coverings and, importantly, emotion, e.g. the use of sunglasses reduced accuracy for recognition of fear in humans. It was also noted that while humans mainly classify unknown expressions as neutral across all coverings, the misclassification varied in the artificial systems. These findings show humans and artificial systems classify and misclassify various emotion expressions differently depending on both the type of face covering and type of emotion.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Different Coverings

Shehu¹,

Wn²,

Eisenbarth³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…With efforts such as the Human Genome Project and Haplotype Map Project, precision medicine, which ‘fundamentally aims to utilize genomic, epigenomic and environmental factors to determine the biological characteristics of a disease and subsequently provide specific individualized treatment to the patient’ (US Food and Drug Administration, 2013), has emerged as an alternative to race-based medicine. Chakravarti (2015: 12), for example, has noted that the introduction of ‘the concept of individualized medicine . .…”

Section: Literature Review: the Debatementioning

confidence: 99%

Between personalized and racialized precision medicine: A relative resources perspective

Sun

2019

International Sociology

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Since the completion of the first human genome sequencing project in 2003, the potential for incorporating genomics into clinical practice in the pursuit of precision medicine has garnered a great amount of attention and interest. Existing literature presents a dichotomous view of the future of medicine: either precision medicine will replace race-based medicine or race-based medicine will persist despite developments in genetic research and genomic medicine. Drawing on interview data with 46 physicians and scientists in the USA, Canada, and Singapore who are conducting research or practicing precision medicine in the context of cancer treatment and prevention, this article attempts to contribute to the existing debate by proposing a ‘relative resources’ perspective to explain which approach will dominate in a particular healthcare setting. The author elaborates on the ‘heterogeneity of resources’ and suggests that the extent to which precision medicine will be personalized or racialized/ethnicized in the clinic will most likely be a function of the relative availability of resources – including but not limited to financial, human and computer informatics, and legal and infrastructural resources – at individual and collective levels in healthcare contexts.

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“…An image that will be classified to have a particular emotion by one person might be classified to have a different emotion by another person. This is due to the different perspectives we all have as a result of variation in neurobiological processes [6]. On the other hand, machine learning classifiers have done well in categorizing emotion when appropriate features are provided to the classifiers.…”

Section: Introductionmentioning

confidence: 99%

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Shehu

Browne

Eisenbarth

2020

Lecture Notes in Computer Science

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Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

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Perspectives on Human Variation through the Lens of Diversity and Race: Figure 1.

Cited by 23 publications

References 39 publications

A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Different Coverings

A Comparison of Humans and Machine Learning Classifiers Detecting Emotion from Faces of People with Different Coverings

Between personalized and racialized precision medicine: A relative resources perspective

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

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