# A PreTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes
## 1 Introduction  
Infrared thermography is a technique to measure heat radiation and, thus, the surface temperature of objects and living beings. In psychological research, thermal imaging is a valuable tool to measure skin temperature variation related to automatic nervous system activity changes [1; 2; 3]. Previous comparative psychological research used thermal imaging to study the emotional states of various animal species [2; 4; 5]. Thermal imaging also finds its application in the human health sector [6; 7; 8] as in the industry [9]. Professor [PERSON] and his team have conducted a series of studies on the retrieval of physiological signals, such as heart rate [10], breath rate [11], and stress detection [12], by utilizing temperature information from the perinasal area of humans. These studies also involve time series analysis using fast Fourier transformations. These studies have applications, among others, in detecting driving distractions [13]. Similarly, in [14], the temperature of the nose tip and forehead is observed to assess cognitive workload while driving. In nonhuman animals, thenose temperature is also observed for detecting arousal [15], breathing, and vocalization [16]. According to these studies, thermal imaging could provide deeper insights into the behavior and cognitive development of nonhuman animals. However, a limitation of thermal imaging is that it does not work through glass or plexiglass. Thus, studies on nonhuman primates are typically conducted with a mesh separating the participating animal from the thermal camera. This not only led to tremendous amounts of data loss (when the animal moved or when areas of interest were covered by bars) but also restricted how data could be processed and analyzed. The limited visibility made automatized processing impossible and forced researchers to process their data manually, leading to significant time loss and imprecise and noisy data. To avoid such limitations, we developed a new apparatus involving infrared transmissive material, which allows an unobscured view of the tested individual while maintaining all necessary safety precautions. This unobscured view also enables us to advance automatized processing of thermal data using deep learning tools. We are developing and comparing different methods to extract the temperatures of areas of interest automatically. Using these newly developed methods, we aim to study small temperature changes related to more subtle emotional changes than previously possible, such as temperature changes in the forehead and nose caused by changes in mental effort. Furthermore, from a continuous signal, we also aim to extract the heart rate and the breath rate of the observed individuals. An accurate heart rate obtained from such a noninvasive acquisition method could also give us more information on their stress state [17] and further details on their nervous system activity [18,19]. Breath rate estimation from thermal images already gave satisfactory results on humans [20] and would also allow a better assessment of the emotional and health state of the observed individuals in a noninvasive way. Due to the rarity of thermal images in image processing, largely attributed to the high cost of recording devices, thermal imaging (TI) is often combined with the RGB modality to enhance resolution [21] or for segmentation and detection purposes [22,23]. However, synchronizing and superimposing the different streams is costly [24] and may not achieve perfect alignment. This often results in trade-offs between the resolution of the modalities, varying acquisition rates, and calibration errors. Bearing these risks in mind, we decided to stick solely to the thermal modality for segmentation and physiological signal extraction to avoid the errors induced by complex cascading processes. In this paper, we present the acquired Ape Thermal Image dataset (ApeTI) and our methods to detect chimpanzees' face and nose landmarks from thermal images.