Automatic Identification of Individual Primates with Deep Learning Techniques

Abstract

Summary The difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

The impacts of heat stress on animal cognition: Implications for adaptation to a changing climate

“…6 To locate individuals, researchers can rely on animal‐borne tracking technology, such as GPS tracking devices (Kays et al, 2015; Rasolofoniaina et al, 2021), or knowledge of the position of key foraging patches (Aplin et al, 2015) or territories (Shaw et al, 2015; McCune et al, 2019). In addition, recent advances in deep learning technology make it possible to automatically identify specific individuals from images and videos (Ferreira et al, 2020; Guo et al, 2020; Hou et al, 2020). For species with limited home ranges, outdoor enclosures could be built on‐site allowing not only easier identification and localization but also manipulation of habitat structure and thus thermal environment (Sears et al, 2016; Li et al, 2017).…”

Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals

“…The method of tracking specific points on faces can also be used to identify individuals and emotional states when it comes to animal subjects. With a little software reconstruction, scientists have been able to create reliable systems for the assessment of animal emotions through technological means [23,24]. These systems have been specified to identify multiple species including, cows, cats, sheep, large carnivores, and many species of non-human primate.…”

“…In studies focusing on identifying individual members of the same species within a group, the accuracy of specialized facial recognition software was found to be between 94% and 98.7%. Some of these studies even displayed the ability of software to identify and categorize new individuals within a group and the ability to identify individuals at night [23][24][25]. Other studies focused more on the emotional expressions that could be identified through facial recognition software and some of the studies showed an accuracy of around 80% when compared to the findings of professionals in the field of animal emotion identification [26].…”

Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

“…Gue [11] suggested automated face detection and individual identification for both videos and still-framed images using deep learning methods. The proposed system was trained and tested with a dataset containing 102,399 images.…”