Face detection and grimace scale prediction of white furred mice

Vidal, Andrea; Jha, Sumit Kumar; Hassler, Shayne; Price, Theodore J.; Busso, Carlos

doi:10.1016/j.mlwa.2022.100312

Cited by 12 publications

(15 citation statements)

References 24 publications

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“…While there are no other automated RGS models published, orbital tightening is used in various animal grimace scales 9 – 13 . Several studies have reported the weighted accuracy of automated classification for orbital tightening in mice, which found values ranging from 0.632 to 0.850 19 , 25 , 32 . All of the current methods of automated orbital tightening grading use convolutional neural networks whereas our model utilizes a ViT.…”

Section: Discussionmentioning

confidence: 99%

An automated rat grimace scale for the assessment of pain

Arnold,

Ramakrishnan,

Wright

et al. 2023

Sci Rep

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Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action units to quantify the pain behaviors of rats. However, manual recording of RGS scores is a time-consuming process that requires training. While computer vision models have been developed and utilized for various grimace scales, there are currently no models for RGS. To address this gap, this study worked to develop an automated RGS system which can detect facial action units in rat images and predict RGS scores. The automated system achieved an action unit detection precision and recall of 97%. Furthermore, the action unit RGS classifiers achieved a weighted accuracy of 81–93%. The system’s performance was evaluated using a blast traumatic brain injury study, where it was compared to trained human graders. The results showed an intraclass correlation coefficient of 0.82 for the total RGS score, indicating that the system was comparable to human graders. The automated tool could enhance pain research by providing a standardized and efficient method for the assessment of RGS.

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Section: Discussionmentioning

confidence: 99%

An automated rat grimace scale for the assessment of pain

Arnold,

Ramakrishnan,

Wright

et al. 2023

Sci Rep

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“…The Viola-Jones object detection framework was used to develop a sheep face detector [30]. A pretrained version of YOLOv3 [31] was modified and tuned for mouse face detection [32]. The authors collected and annotated 2,222 images to train the model.…”

Section: Related Workmentioning

confidence: 99%

AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection

Kuzdeuov

Koishigarina

Varol

2023

2023 IEEE International Conference on Big Data and Smart Computing (BigComp)

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Face detection is a mandatory step in many computer vision applications, such as face recognition, emotion recognition, age detection, virtual makeup, and vital sign monitoring. Thanks to advancements in deep learning and the introduction of annotated large-scale datasets, numerous applications have been developed for human faces. Recently, other domains, such as animals and cartoon characters, have started gaining attention but still lag far behind human faces. The biggest challenge is the limited number of annotated face datasets in these domains. The manual labeling of large-scale datasets is tedious and requires substantial human labor. In this regard, we present an inputagnostic face detector to ease the annotation of various face datasets. We propose a simple but effective data-centric approach instead of building a specific neural network architecture. Specifically, we trained a face detection model, YOLO5Face, on human, animal, and cartoon face datasets. The experiments show that the model can achieve accurate results in all domains. In addition, the model achieved decent results for animals and cartoon characters different from the ones in the training set. This implies that the model can extract agnostic facial features. We have made the source code and pre-trained models publicly available at https://github.com/IS2AI/AnyFace to stimulate research in these fields.

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Section: Related Workmentioning

confidence: 99%

AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection

Kuzdeuov¹,

Koishigarina²,

Varol³

2022

Preprint

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<p>Face detection is a mandatory step in many computer vision applications, such as face recognition, emotion recognition, age detection, virtual makeup, and vital sign monitoring. Thanks to advancements in deep learning and the introduction of annotated large-scale datasets, numerous applications have been developed for human faces. Recently, other domains, such as animals and cartoon characters, have started gaining attention but still lag far behind human faces. The biggest challenge is the limited number of annotated face datasets in these domains. The manual labeling of large-scale datasets is tedious and requires substantial human labor. In this regard, we present an input-agnostic face detector to ease the annotation of various face datasets. We propose a simple but effective data-centric approach instead of building a specific neural network architecture. Specifically, we trained a face detection model, YOLO5Face, on human, animal, and cartoon face datasets. The experiments show that the model can achieve accurate results in all domains. In addition, the model achieved decent results for animals and cartoon characters different from the ones in the training set. This implies that the model can extract agnostic facial features. We have made the source code and pre-trained models publicly available at https://github.com/IS2AI/AnyFace to stimulate research in these fields.</p>

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Face detection and grimace scale prediction of white furred mice

Cited by 12 publications

References 24 publications

An automated rat grimace scale for the assessment of pain

An automated rat grimace scale for the assessment of pain

AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection

AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection

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