There is a TON of research on ML algorithms for all this!

For facial rec-

BearID project - http://bearresearch.org/ (individual facial rec for grizzlies)

AnimalFACS (facial action coding system) - http://www.chimpfacs.com/ (the model started with chimps but they now have versions for orangutans, gibbons, macaques, dogs, cats, and horses)

A lot of the gesture/facial expression literature is focused on primates, particularly apes, so if you do a Google Scholar search on that, you'll loads of papers that have looked into this as well.

For vocals - 

Carlson, N. V., Kelly, E. M., & Couzin, I. (2020). Individual vocal recognition across taxa: a review of the literature and a look into the future. Philosophical Transactions of the Royal Society B, 375(1802), 20190479.

Turesson, H. K., Ribeiro, S., Pereira, D. R., Papa, J. P., & de Albuquerque, V. H. C. (2016). Machine learning algorithms for automatic classification of marmoset vocalizations. PloS one, 11(9), e0163041.

Wijers, M., Trethowan, P., Du Preez, B., Chamaillé-Jammes, S., Loveridge, A. J., Macdonald, D. W., & Markham, A. (2020). Vocal discrimination of African lions and its potential for collar-free tracking. Bioacoustics, 1-19.

Clink, D. J., Crofoot, M. C., & Marshall, A. J. (2019). Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia. Bioacoustics, 28(3), 193-209.

Spillmann, B., van Schaik, C. P., Setia, T. M., & Sadjadi, S. O. (2017). Who shall I say is calling? Validation of a caller recognition procedure in Bornean flanged male orangutan (Pongo pygmaeus wurmbii) long calls. Bioacoustics, 26(2), 109-120.

Hantke, S., Cummins, N., & Schuller, B. (2018, April). What is my dog trying to tell me? The automatic recognition of the context and perceived emotion of dog barks. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5134-5138). IEEE.

Totakura, V., Janmanchi, M. K., Rajesh, D., & Hussan, M. T. (2020). Prediction of Animal Vocal Emotions Using Convolutional Neural Network. International Journal of Scientific & Technology Research, 9(2), 6007-6011.

Other - 

Neethirajan, S., Reimert, I., & Kemp, B. (2021). Measuring Farm Animal Emotions—Sensor-Based Approaches. Sensors, 21(2), 553.

Hong, W., Kennedy, A., Burgos-Artizzu, X. P., Zelikowsky, M., Navonne, S. G., Perona, P., & Anderson, D. J. (2015). Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning. Proceedings of the National Academy of Sciences, 112(38), E5351-E5360.

Hello,

Just spit-balling some ideas, but you might check out WildMe/WildBook, Widlife Insights, the ICARUS/Movebank project, Arbimon, Norway's new imaging initiative (https://www.planet.com/nicfi/), Hack the Poacher, Vulcan's EarthRanger, SmartParks, the SMART app/platform, the TEAM camera trap network?

Not sure if any of this is what you're looking for but just some things that came to mind! 

Dan's comments about the need for technologists and conservationists to manage and share (properly annotated) data struck a chord with me, it was right at the end of the presentation.

I fired off a point into the chat "could your old background data be my background data?" or something similar, and it got me thinking...

(Firstly, sorry for clouding this issue with my simultaneous "Ian Tuna" joke)

In the context of using AI as described in EdgeImpulse example, lets say - audio.

Lets say Group A are trying to detect the roar of lions, and Group B are trying to detect the grunt of wild pigs, and lets imagine that is in roughly the same area of Africa.  I mean they could both send out teams to capture the sound of their target animal, and the sound of NOT their target animal, e.g everything else.

Well if "serengetti sounds" was a known audio track, then all you need is the unique sound of the beast in question, am I right?