Affective State Recognition using Image Cues

Reddy, R Pradeep; Krishna, P. Murali; Narayanan, V.G.; Lalitha, S.

doi:10.1109/icacci.2018.8554441

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Semi-supervised: Systems that rely on supervised machine learning require large amounts of labeled training data (e.g. (Michel and El Kaliouby 2003), (Reddy et al 2018)). Labeling affective states by hand is costly and timeconsuming.…”

Section: Introduction and Relation To Prior Workmentioning

confidence: 99%

Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor

Agarwal

Mostow

2020

AAAI

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Like good human tutors, intelligent tutoring systems should detect and respond to students' affective states. However, accuracy in detecting affective states automatically has been limited by the time and expense of manually labeling training data for supervised learning. To combat this limitation, we use semi-supervised learning to train an affective state detector on a sparsely labeled, culturally novel, authentic data set in the form of screen capture videos from a Swahili literacy and numeracy tablet tutor in Tanzania that shows the face of the child using it. We achieved 88% leave-1-child-out cross-validated accuracy in distinguishing pleasant, unpleasant, and neutral affective states, compared to only 61% for the best supervised learning method we tested. This work contributes toward using automated affect detection both off-line to improve the design of intelligent tutors, and at runtime to respond to student affect based on input from a user-facing tablet camera or webcam.

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Section: Introduction and Relation To Prior Workmentioning

confidence: 99%

Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor

Agarwal

Mostow

2020

AAAI

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Multilingual and Mixed-lingual Digit Speech Recognition System for Indian Context

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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The current challenges of automatic recognition of facial expressions: A systematic review

Masson

Cazenave²,

Trombini³

et al. 2020

AIC

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In recent years, due to its great economic and social potential, the recognition of facial expressions linked to emotions has become one of the most flourishing applications in the field of artificial intelligence, and has been the subject of many developments. However, despite significant progress, this field is still subject to many theoretical debates and technical challenges. It therefore seems important to make a general inventory of the different lines of research and to present a synthesis of recent results in this field. To this end, we have carried out a systematic review of the literature according to the guidelines of the PRISMA method. A search of 13 documentary databases identified a total of 220 references over the period 2014–2019. After a global presentation of the current systems and their performance, we grouped and analyzed the selected articles in the light of the main problems encountered in the field of automated facial expression recognition. The conclusion of this review highlights the strengths, limitations and main directions for future research in this field.

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Affective State Recognition using Image Cues

Cited by 4 publications

References 26 publications

Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor

Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor

Multilingual and Mixed-lingual Digit Speech Recognition System for Indian Context

The current challenges of automatic recognition of facial expressions: A systematic review

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