Evidence map›Paper›PMID 28739161›Full record

ArticleJournal of neuroscience methods2018

Automated face recognition of rhesus macaques.

Claire L Witham

Abstract read
In one paragraph

Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Claire L WithamInstitute of Neuroscience, Newcastle University, Newcastle-upon-Tyne, UK; Centre for Macaques, Medical Research Council, UK. Electronic address: claire.witham@ncl.ac.uk.

Funding

Medical Research Council
6 · The paper itself

Abstract

backgroundRhesus macaques are widely used in biomedical research. Automated behavior monitoring can be useful in various fields (including neuroscience), as well as having applications to animal welfare but current technology lags behind that developed for other species. One difficulty facing developers is the reliable identification of individual macaques within a group especially as pair- and group-housing of macaques becomes standard. Current published methods require either implantation or wearing of a tracking device. NEW

methodI present face recognition, in combination with face detection, as a method to non-invasively identify individual rhesus macaques in videos. The face recognition method utilizes local-binary patterns in combination with a local discriminant classification algorithm.

resultsA classification accuracy of between 90 and 96% was achieved for four different groups. Group size, number of training images and challenging image conditions such as high contrast all had an impact on classification accuracy. I demonstrate that these methods can be applied in real time using standard affordable hardware and a potential application to studies of social structure. COMPARISON WITH EXISTING METHOD(S): Face recognition methods have been reported for humans and other primate species such as chimpanzees but not rhesus macaques. The classification accuracy with this method is comparable to that for chimpanzees. Face recognition has the advantage over other methods for identifying rhesus macaques such as tags and collars of being non-invasive.

conclusionsThis is the first reported method for face recognition of rhesus macaques, has high classification accuracy and can be implemented in real time.

Indexed as

Facial RecognitionMacaca mulattaAnimalsArtificial IntelligenceImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMonitoring, AmbulatoryPattern Recognition, AutomatedSupport Vector MachineComputer visionFace detectionFace recognitionMonkey

Identifiers

PMID28739161
PMCPMC5909037

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.