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?

One barrier that was discussed in the presentation was the "risk" or uncertainty of a payoff when talking to potential funders. It seems to me that the "risk" factor of collaborations like this, for funders, would be analogous to the investment and payoff when developing a genomics program. Relative to traditional field research, it takes a much greater investment in time, with different expertise, to create a reference genome for a species of interest and then pull SNPs, etc. Nevertheless, funders seem to recognize the potential for genomic research and genomic tools. Do you think that greater openness to “risk” in conservation genomics results from closer intellectual proximity (i.e., molecular biology vs organismal biology), or to conservation genomics being a more mature field (i.e., more examples of success), or to other factors? If intellectual proximity is a key factor, then conservation tech collaborations will continue to face this barrier, but if lack of successful examples are a key factor, then perhaps this barrier will become less of an issue for conservation tech in the future. Thoughts?

Hi. Just watched the Youtube version (I'm in the unable to watch live hemisphere) and wanted to say that was a great talk. I wish it could have gone for another hour. I liked hearing about the issues and concerns from Melanie on the biology/ecology side. Also could totally relate to what Ed was talking about from the tech side about how it's difficult to have long term collaborations since there isn't really a conservation technology career path at the moment. 
I think one of the things we're trying to do with courses like Build Your Own Datalogger is to get people involved in wildlife/conservation side more comfortable with the tech. The goal is to hopefully build a shared repository of technical knowledge that people in the wildlife community can contribute to and benefit from. 

Akiba