Landmark calibration for facial expressions and fish classification

Chaturvedi, Iti, Cambria, Erik, Chen, Qian, and McConnell, Desmond (2021) Landmark calibration for facial expressions and fish classification. Signal, Image and Video Processing. (In Press)

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Abstract

This paper considers the automatic labeling of emotions in face images found on social media. Facial landmarks are commonly used to classify the emotions from a face image. However, it is difficult to accurately segment landmarks for some faces and for subtle emotions. Previous authors used a Gaussian prior for the refinement of landmarks, but their model often gets stuck in a local minima. Instead, the calibration of the landmarks with respect to the known emotion class label using principal component analysis is proposed in this paper. Next, the face image is generated from the landmarks using an image translation model. The proposed model is evaluated on the classification of facial expressions and also for fish identification underwater and outperforms baselines in accuracy by over 20%.

Item ID: 68485
Item Type: Article (Research - C1)
ISSN: 1863-1711
Keywords: Facial expressions, Adversarial training, Singular value decomposition, Fish segmentation
Copyright Information: © Crown 2021
Date Deposited: 09 Jul 2021 03:43
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 50%
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3005 Fisheries sciences > 300501 Aquaculture @ 25%
52 PSYCHOLOGY > 5204 Cognitive and computational psychology > 520499 Cognitive and computational psychology not elsewhere classified @ 25%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180501 Assessment and management of benthic marine ecosystems @ 25%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220407 Human-computer interaction @ 25%
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