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DogFLW: Dog Facial Landmarks in the Wild Dataset (2405.11501v1)

Published 19 May 2024 in cs.CV

Abstract: Affective computing for animals is a rapidly expanding research area that is going deeper than automated movement tracking to address animal internal states, like pain and emotions. Facial expressions can serve to communicate information about these states in mammals. However, unlike human-related studies, there is a significant shortage of datasets that would enable the automated analysis of animal facial expressions. Inspired by the recently introduced Cat Facial Landmarks in the Wild dataset, presenting cat faces annotated with 48 facial anatomy-based landmarks, in this paper, we develop an analogous dataset containing 3,274 annotated images of dogs. Our dataset is based on a scheme of 46 facial anatomy-based landmarks. The DogFLW dataset is available from the corresponding author upon a reasonable request.

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References (52)
  1. Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration. Scientific reports, 12(1):22611, 2022.
  2. Evaluating the accuracy of facial expressions as emotion indicators across contexts in dogs. Animal cognition, pages 1–16, 2021.
  3. Differences in facial expressions during positive anticipation and frustration in dogs awaiting a reward. Scientific reports, 9(1):1–13, 2019.
  4. Going deeper than tracking: A survey of computer-vision based recognition of animal pain and emotions. International Journal of Computer Vision, 131(2):572–590, 2023.
  5. Erickson R. Nascimento Bruna Vieira Frade. A two-step learning method for detecting landmarks on faces from different domains. In Proceedings of the 2018 IEEE International Conference on Image Processing (ICIP), 2018.
  6. Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. ICCV, 2017.
  7. Robust face landmark estimation under occlusion. In 2013 IEEE International Conference on Computer Vision, pages 1513–1520, 2013.
  8. Dogs and humans respond to emotionally competent stimuli by producing different facial actions. Scientific reports, 7(1):1–11, 2017.
  9. Development and application of catfacs: Are human cat adopters influenced by cat facial expressions? Applied Animal Behaviour Science, 2017.
  10. Cross-domain adaptation for animal pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9498–9507, 2019.
  11. Structured feature learning for pose estimation. CVPR, 2016.
  12. Extending the MaqFACS to measure facial movement in japanese macaques (macaca fuscata) reveals a wide repertoire potential. PLOS ONE, 16(1):e0245117, 2021.
  13. CVAT.ai Corporation. Computer Vision Annotation Tool (CVAT), Nov. 2023.
  14. Facial expression: An under-utilized tool for the assessment of welfare in mammals. ALTEX-Alternatives to animal experimentation, 34(3):409–429, 2017.
  15. Facial Action Coding System: Manual. Palo Alto, Calif: Consulting Psychologists Press, 1978.
  16. Can my human read my flat face? the curious case of understanding the contextual cues of extremely brachycephalic dogs. Applied Animal Behaviour Science, 270:106134, 2024.
  17. Facial expressions of pain in cats: the development and validation of a feline grimace scale. Scientific reports, 9(1):1–11, 2019.
  18. Explainable automated pain recognition in cats. Scientific reports, 13(1):8973, 2023.
  19. Automated recognition of pain in cats. Scientific Reports, 12, 2022.
  20. Predicting dog emotions based on posture analysis using deeplabcut. Future Internet, 14(4):97, 2022.
  21. Geometric morphometrics for the study of facial expressions in non-human animals, using the domestic cat as an exemplar. Scientific reports, 9(1):1–12, 2019.
  22. The application of geometric morphometrics to explore potential impacts of anthropocentric selection on animals’ ability to communicate via the face: The domestic cat as a case study. Frontiers in Veterinary Science, page 1070, 2020.
  23. Pfld: A practical facial landmark detector. CoRR, 2019.
  24. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. Journal of neuroscience methods, 200(2):237–256, 2011.
  25. Deep residual learning for image recognition. CVPR, 2016.
  26. Pose-informed face alignment for extreme head pose variations in animals. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pages 1–6. IEEE, 2019.
  27. Canine models of human rare disorders. Rare Diseases, 4(1):e1006037, 2016.
  28. Pixel-in-pixel net: Towards efficient facial landmark detection in the wild. International Journal of Computer Vision, 2021.
  29. YOLO by Ultralytics, 2023.
  30. Animalweb: A large-scale hierarchical dataset of annotated animal faces. CVRR, abs/1909.04951, 2019.
  31. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, June 2011.
  32. Structured landmark detection via topology-adapting deep graph learning. ECCV, 2020.
  33. Dog breed classification using part localization. In European conference on computer vision, pages 172–185. Springer, 2012.
  34. Yong Jae Lee M. Rashid, Xiuye Gu. Interspecies knowledge transfer for facial keypoint detection. CVPR, 2017.
  35. The new era of canine science: reshaping our relationships with dogs. Frontiers in Veterinary Science, page 762, 2021.
  36. Catflw: Cat facial landmarks in the wild dataset. arXiv preprint arXiv:2305.04232, 2023.
  37. Automated pain recognition in cats using facial landmarks: Dynamics matter. 2023. in review.
  38. Automated landmark-based cat facial analysis and its applications. 2023. in review.
  39. Automated detection of cat facial landmarks. International Journal of Computer Vision, pages 1–16, 2024.
  40. A deep learning approach for dog face verification and recognition. Lecture Notes in Computer Science, 2019.
  41. Audience effect on domestic dogs’ behavioural displays and facial expressions. Scientific Reports, 12(1):9747, 2022.
  42. Facial image-based automatic assessment of equine pain. IEEE Transactions on Affective Computing, 2022.
  43. Mobilenetv2: Inverted residuals and linear bottlenecks. CVPR, 2018.
  44. What is written on a dog’s face? evaluating the impact of facial phenotypes on communication between humans and canines. Animals, 13(14):2385, 2023.
  45. Cafm: A 3d morphable model for animals. IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020.
  46. Deep convolutional network cascade for facial point detection. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 3476–3483, 2013.
  47. Efficientnet: Rethinking model scaling for convolutional neural networks. CoRR, abs/1905.11946, 2019.
  48. Paedomorphic facial expressions give dogs a selective advantage. PLoS one, 8(12):e82686, 2013.
  49. Anchorface: An anchor-based facial landmark detector across large poses. Thirty-Fifth AAAI Conference on Artifi- cial Intelligence, AAAI 2021, Thirty-Third Confer- ence on Innovative Applications of Artificial Intelli- gence, IAAI 2021, The Eleventh Symposium on Ed- ucational Advances in Artificial Intelligence, EAAI 2021, 2021.
  50. Human and sheep facial landmarks localisation by triplet interpolated features. CVRR, abs/1509.04954, 2015.
  51. Cat head detection - how to effectively exploit shape and texture features. ECCV, 2008.
  52. Learning and transferring multi-task deep representation for face alignment. CoRR, abs/1408.3967, 2014.
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Authors (8)
  1. George Martvel (4 papers)
  2. Greta Abele (1 paper)
  3. Annika Bremhorst (2 papers)
  4. Chiara Canori (1 paper)
  5. Nareed Farhat (3 papers)
  6. Giulia Pedretti (1 paper)
  7. Ilan Shimshoni (9 papers)
  8. Anna Zamansky (16 papers)

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