# A PreTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes
## 6 Conclusions  
With this new ApeTI dataset, despite its limited size, we managed to fine-tune pre-trained models for face detection and landmark regression. We evaluated and compared their performances and built a pipeline for finding and tracking landmarks of interest. The annotated landmarks were chosen for their aptitude to retrieve physiological signals with the aim of better understanding a chimpanzee's behavior. The introduced Tina and Tifa models performed best compared with similar tools. The lack of models trained on thermal images did not allow an exhaustive comparison. Our future work will focus on the enlargement of the dataset to obtain models more robust to individual differences and acquisition angles, and the comparison of a greater number of models. Our final goal being the processing of thermal images for biosignal retrieval for the study of human and nonhuman animal behaviors in an ecological context, several challenges remain to be solved, such as the following:  
* Metal mesh segmentation and removal from thermal images;
* Heart rate and breath rate estimation from thermal videos; and
* Cognitive load estimation and monitoring from thermal videos. The recording of physiological signals in nonhuman primates without invasive instruments or protocols is not trivial [36] and makes the evaluation of computed physiological signals hard to perform. Therefore, we are planning to record a new set of thermal videos synchronized with devices to record the heart rate and the breath rate of humans. This dataset will allow us to develop and evaluate methods to calculate such rates from the thermal information and define which regions of interest have to be observed for computing such rates. It may also contribute to the creation of a facial mapping proper to biosignal retrieval and tailor our annotation efforts for training new regression models. As presented in Section 5, we hope to retrieve these biosignals automatically from the thermal stream and transfer our knowledge from humans to other nonhuman animal species. Conceptualization, P.-E.M., G.K., J.E. and D.B.M.H. ; methodology, P.-E.M.; software, P.-E.M.; validation, P.-E.M.; formal analysis, P.-E.M.; investigation, P.-E.M. and J.E. ; resources, D.B.M.H. ; data curation, P.-E.M., G.K. and N.W. ; writing--original draft preparation, P.-E.M.; writing--review and editing, P.-E.M.; visualization, P.-E.M.; supervision, P.-E.M., G.K., J.E. and D.B.M.H. ; project administration, P.-E.M.; funding acquisition, D.B.M.H. All authors have read and agreed to the published version of the manuscript. Figure 9: An attempt of biosignal retrieval from a thermal video with a human face using BlazeFace [34] and FaceMesh [35] for segmentation. **Funding:** This research received no external funding. **Institutional Review Board Statement:** The study involving great apes was reviewed and approved by an ethics committee from the Max Planck Institute for Evolutionary Anthropology and the Leipzig Zoo. This committee was internal and newly created at the time of the study; therefore, no approval number was delivered. The procedures used comply with the Weatherfall report \"The use of non-human primates in research\" and with the EAZA Minimum Standards for the Accommodation and Care of Animals in Zoos and Aquaria, the WAZA Ethical Guidelines for the Conduct of Research on Animals by Zoos and Aquariums, and the ASAB/ABS's Guidelines for the Treatment of Animals in Behavioural Research and Teaching. IACUC approval was not necessary to conduct this research. Testing was always voluntary, the individuals were not food- or water-deprived, and a trained animal caretaker did the handling. Returning to their social group was possible at any moment of the test, as well as terminating the test, giving signs of discomfort or distress. We did not separate infants from their mothers.