# data[].attributes.body
Scientists have been able to identify certain individuals through natural patterns on animals such as cheetahs, giraffe, orca, whale sharks, etc. The benefit to this is that an individual animal can then be tracked via a photo library that can be accessed over the corse of that individuals life (Speed et. al., 2007).

However, when making photo identification, quality is key. Smaller animals require a certain lens for accurate identification (Escobar et. al., 2018).

# data[].attributes.body
Photo retrieved from: https://sciencenode.org/feature/Wildbook.phphttps://sciencenode.org/feature/Wildbook.php. 3/3/19.

# data[].attributes.body
With the advent of cell phone cameras, more and more wildlife photos are becoming available all over the world. Researchers have started to recruit citizen scientists to gather photos both at home or when abroad (Escobar et. al., 2018).

The creation of systems such as Wildbook, were photos can be added to a digital library for identification. With the use of citizen scientists, researchers are capable of collecting more photos of individual animals and at a large scale across geographical locations. In fact, in 2016, 40,000 photographs were loaded into Wildbook via a collective effort between researchers, school children, tribal chiefs, and ambassadors in Kenya. These photos focused of Grevy's Zebra, whos numbers had counted on a small scale. Due to the large scale collaborative effort via Wildbook, the most accurate data of Grevy's Zebra's was collected and is now recognized on the IUCN Red List of Threatened Species (Reed, 2018).

# data[].attributes.body
Photo identification has enabled wildlife researches to assess population density and biodiversity. In a study by Debata and Swain (2018), researchers were able to determine the diversity and abundance of "naturally marked" (i.e. stripe, spot, or notching patterns) animals in eastern India.

This brings up the question, what about animals, such as antelope, rhino, or other animals that lack natural markings? Well, with the development of new technologies and algorithms, identifying animals of every species (Timmins, 2019) is becoming a real possibility.

# data[].attributes.body
Photo retrieved from: Retrieved from: https://www.bbc.com/news/science-environment-46945302. 3/3/19.

# data[].attributes.body
One tool that researchers have researchers have started to develop and utilize for wildlife photo identification is facial recognition.

Facial recognition is most commonly used to match and record identity of individuals, most commonly, people (Wick, 2018). However, recent developments may now help in illegal wildlife trafficking (Thomas, 2018) and the identification of individuals that do not have natural markings, such as elephants (Timmins, 2019).

Most predominately, facial recognition has been used to identify trafficked chimpanzees by using the program ChimpFace using photos of chimpanzees from the social media of known or possible traffickers (Timmins, 2019).

# data[].attributes.body
Wildbook: Our Vision in One Minute by Wild Me. Retrieved from: https://travelconserve.wordpress.com/2019/03/03/listen-up-and-conserve/. 3/3/19.

# data[].attributes.body
Photo retrieved from: https://www.asmag.com/showpost/25402.aspx. 3/3/2019.

# data[].attributes.body
Taking photos and going through photos is a slow process and takes a lot of man/woman power. In order to have an accurate data library, many photos must be retrieved and accurately identified (Speed et. al., 2007). Additionally, relatively detailed photos must be taken to properly identify an individual (Escobar, 2018).

# data[].attributes.body
Identifying wildlife through photos has proven to be a relatively cheap, useful, and nonintrusive way to both track and identify various species (Brooks et. al., 2010). Scientists have utilized photographic information to determine population, species abundance, and to track individuals (Debata and Swain, 2018). The development of new technologies has also allowed for photos to be identified faster (Speed et. al., 2007)  and has utilized citizen science (Escobar et. al, 2018) to gather even more information than ever before!

# data[].attributes.body
Now that millions, upon millions, of photos are capable of being uploaded to digital photo databases by literally anyone, who is going to go through all those photos?

There in lays the beauty of new technological development. Algorithms have been created to go through photos at lightning speed. In one instance, a algorithm was developed and processed 375,000 images at 2,000 images a minute with 97.6% accuracy (Sandle, 2018).

More photos means more data available to researchers. This algorithms allow researchers to determine species, number of animals, and what the animals are up to (Furness, 2018).

# data[].attributes.body
As technology becomes more readily available to everyone, so does the data that has been added to various databases. While being a valuable tool to researchers, the availability of an individual animals location and the time of which the photo was taken is also valuable to poachers (Reed, 2018).

Luckily, measures are being taken to protect animal identities. In fact, Wildbook has partnered with a technology company to protect its information from falling into the wrong hands (Reed, 2019).

# data[].attributes.attachment_link.title
Citizens and scientists earn their stripes counting Kenya's Grevy's zebras

# data[].attributes.body
Citizens and scientists earn their stripes counting Kenya's zebras by Euronews Knowledge. Retrieved from: https://sciencenode.org/feature/Wildbook.phphttps://sciencenode.org/feature/Wildbook.php. 3/3/19.

# data[].attributes.body
Brooks, Katie, et. al. (2010). Seeing Spots: Photo-identification as a Regional Tool for Whale Shark Identification. Western Indian Ocean Journal, 9:2 185-194.

Debata, Subrat, and Swain, Kedra Kumar. (2018). Estimating mammalian diversity and relative abundance using camera traps in a tropical deciduous forest of Kuldiha Wildlife Sanctuary, eastern India. The Mammal Society of Japan, 43: 45-53.

Escobar, Javier, et. al. (2018). Telescoping turtles: a comparison of smartphone telephoto magnifiers to non-invasively observe and identify freshwater turtles. Herpetological Journal, 28: 143-147.

Furness, Dyllan. (2018). One fish, two fish: A.I. labels wildlife photos to boost conservation. Digitaltrends.com. Retrieved from: https://www.digitaltrends.com/cool-tech/ai-animal-photograph/.

Reed, Laura. (2018). Combating extinction one photo at a time. Sciencenode.org. Retrieved from: https://sciencenode.org/feature/Wildbook.php.

Sandle, Tim. (2018). Using AI to identify animals from photos. Digitaljournal.com. Retrieved from: http://www.digitaljournal.com/news/environment/using-ai-to-identify-animals-from-photos/article/538150.

Speed, Conrad W, et. al. (2007). Spot the match - wildlife photo-identification using information theory. Frontiers in Zoology, 4:2.

Timmins, Beth. (2019). Facial recognition tool tackles illegal chimp trade. BBC.com. Retrieved from: https://www.bbc.com/news/science-environment-46945302.

Thomas, Prasanth Aby. (2018). Beyond humans: how facial recognition helps in wildlife preservation. Asmag.com. Retrieved from: https://www.asmag.com/showpost/25402.aspx.

Wick, Don. (2018). Visage Quest: The use of facial recognition software in law enforcement has benefits and drawbacks. Sheriff & Deputy, Pp. 78-79.