AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

Being heavily reliant on animals, it is our ethical obligation to improve their well-being by understanding their needs. Several studies show that animal needs are often expressed through their faces. Though remarkable progress has been made towards the automatic understanding of human faces, this has regrettably not been the case with animal faces. There exists significant room and appropriate need to develop automatic systems capable of interpreting animal faces. Among many transformative impacts, such a technology will foster better and cheaper animal healthcare, and further advance animal psychology understanding. We believe the underlying research progress is mainly obstructed by the lack of an adequately annotated dataset of animal faces, covering a wide spectrum of animal species. To this end, we introduce a large-scale, hierarchical annotated dataset of animal faces, featuring 21.9K faces from 334 diverse species and 21 animal orders across biological taxonomy. These faces are captured `in-the-wild' conditions and are consistently annotated with 9 landmarks on key facial features. The proposed dataset is structured and scalable by design; its development underwent four systematic stages involving rigorous, manual annotation effort of over 6K man-hours. We benchmark it for face alignment using the existing art under novel problem settings. Results showcase its challenging nature, unique attributes and present definite prospects for novel, adaptive, and generalized face-oriented CV algorithms. We further benchmark the dataset for face detection and fine-grained recognition tasks, to demonstrate multi-task applications and room for improvement. Experiments indicate that this dataset will push the algorithmic advancements across many related CV tasks and encourage the development of novel systems for animal facial behaviour monitoring. We will make the dataset publicly available

Automatic detection and classification of head movements in face-to-face conversations

This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-toface conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, the acoustic ones from the sound files using Praat, and the word features from the transcriptions. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline, a more advanced baseline only relying on velocity features, and linear classifiers using different combinations of featurespeer-reviewe

Narrowing the gap between eye care needs and service provision: the service-training nexus

<p>Abstract</p> <p>Background</p> <p>The provision of eye care in the developing world has been constrained by the limited number of trained personnel and by professional cultures. The use of personnel with specific but limited training as members of multidisciplinary teams has become increasingly important as health systems seek to extract better value from their investments in personnel. Greater positive action is required to secure more efficient allocation of roles and resources. The supply of professional health workers is a factor of the training system, so it stands to reason that more cost-effective, flexible and available education methods are needed. This paper presents a highly flexible competencies-based multiple entry and exit training system that matches and adapts training to the prevailing population and service needs and demands, while lifting overall standards over time and highlighting the areas of potential benefit.</p> <p>Methods</p> <p>Literature surveys and interviews in five continents were carried out. Based on this and the author's own experience, a encies-based multiple entry and exit scheme for eye care in a developing country was derived, modeled and critically reviewed by interested parties in one country.</p> <p>Results</p> <p>The scheme was shown to be highly cost-effective and readily adaptable to the anticipated eye care needs of the population. Eye care players in one selected country have commented favourably on the scheme.</p> <p>Conclusion</p> <p>The underlying principles used to derive this model can be applied to many eye care systems in many developing countries. The model can be used in other disciplines with similar constructs to eye care.</p