2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) 2020
DOI: 10.1109/fg47880.2020.00107
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Towards automatic monitoring of disease progression in sheep: A hierarchical model for sheep facial expressions analysis from video

Abstract: Pain in farm animals harms the economics of farming and affects animal welfare. However, prey animals tend to not openly express signs of weakness, making the pain assessment process difficult. We propose a novel hierarchical model for disease progression evaluation, adapted for a wide range of head poses, according to which relevant information is extracted. A fine-tuned CNN is applied for face detection, followed by a CNN-based pose estimation and pose-informed landmark location method. Then multi-modal feat… Show more

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Cited by 17 publications
(18 citation statements)
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“…In [40], the work in [38] is continued, using automatically recognized sheep facial landmarks to assess pain on a single-frame basis. In this work, disease progression is monitored from video data, by applying the same pipeline on every 10th frame, and averaging their pain scores.…”
Section: Automatic Pain Recognition In Animalsmentioning
confidence: 99%
“…In [40], the work in [38] is continued, using automatically recognized sheep facial landmarks to assess pain on a single-frame basis. In this work, disease progression is monitored from video data, by applying the same pipeline on every 10th frame, and averaging their pain scores.…”
Section: Automatic Pain Recognition In Animalsmentioning
confidence: 99%
“…In this section, the meta-analysis of the works in Table 1 is organized according to the different stages of a typical workflow in studies within this domain: data collection and annotation, followed by data analysis (typically, model training and inference) and last, performance evaluation. For each of these stages, we classify the methods and techniques applied in these [76] unknown or naturally occurring face + Lencioni et al [77] Horses pain surgical castration face + Hummel et al [38] unknown or induced pain face + Broomé et al [78] induced pain body and face + Broomé et al [79] induced pain body and face + Rashid et al [80] induced pain body + Reulke et al [81] vet. procedure body -Corujo et al [82] emotion unknown body and face + Li et al [83] --face ---Feightelstein et al [84] Cats pain vet.…”
Section: Meta-analysis Of Computer Vision-based Approaches For Classi...mentioning
confidence: 99%
“…With the promising results from the manually annotated EquiFACS datasets [51,52], we investigated methods for automated recognition of horse facial AUs in still images [110]. Previous work has explored automated detection of keypoint-based facial expression information, but in a simplified form compared to, for example, EquiFACS [111][112][113]. In studies by the authors of [111,112], the method learned to classify the appearance of certain facial areas directly in terms of pain, meaning the presence of a specific AU, was not determined.…”
Section: Automated Detection Of Facial Action Unitsmentioning
confidence: 99%
“…This was in fact the first successful attempt to perform pain recognition in any nonhuman species by extracting spatiotemporal patterns from video data. Since then, Pessanha et al [113] have explored the temporal dimension in pain recognition and disease progression monitoring from video data of sheep. Their study was performed on animals in the wild, over a time span of one month, although the image processing was done at frame level and classifications were subsequently averaged over time.…”
Section: Automated Pain Detection Using Temporal Informationmentioning
confidence: 99%