Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears. Issue 23 (6th November 2020)

• Record Type: Journal Article
• Authors: Clapham, Melanie; Miller, Ed; Nguyen, Mary; Darimont, Chris T.
• Is Part Of: Ecology and evolution. Volume 10:Issue 23(2020)
• Page Start: 12883
• Page End: 12892
• Publication Date: 2020-11-06
• Subjects: deep learning -- face recognition -- grizzly bear -- individual ID -- machine learning -- wildlife monitoring; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.6840
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Emerging technologies support a new era of applied wildlife research, generating data on scales from individuals to populations. Computer vision methods can process large datasets generated through image‐based techniques by automating the detection and identification of species and individuals. With the exception of primates, however, there are no objective visual methods of individual identification for species that lack unique and consistent body markings. We apply deep learning approaches of facial recognition using object detection, landmark detection, a similarity comparison network, and an support vector machine‐based classifier to identify individuals in a representative species, the brown bear Ursus arctos . Our open‐source application, BearID, detects a bear's face in an image, rotates and extracts the face, creates an "embedding" for the face, and uses the embedding to classify the individual. We trained and tested the application using labeled images of 132 known individuals collected from British Columbia, Canada, and Alaska, USA. Based on 4, 674 images, with an 80/20% split for training and testing, respectively, we achieved a facial detection (ability to find a face) average precision of 0.98 and an individual classification (ability to identify the individual) accuracy of 83.9%. BearID and its annotated source code provide a replicable methodology for applying deep learning methods of facial recognition applicable to many other species that lack distinguishing markings. Further analyses of performance should focus on the influence of certain parameters on recognition accuracy, such as age and body size. Combining BearID with camera trapping could facilitate fine‐scale behavioral research such as individual spatiotemporal activity patterns, and a cost‐effective method of population monitoring through mark–recapture studies, with implications for species and landscape conservation and management. Applications to practical conservation include identifying problem individuals in human–wildlife conflicts, and evaluating the intrapopulation variation in efficacy of conservation strategies, such as wildlife crossings. Abstract : One challenge of image‐based ecological studies is the inability to distinguish among individuals within species that lack unique markings. We describe an automated method to detect and identify individual brown bears ( Ursus arctos ), using facial recognition. Our application and detailed pipeline provide an important step in harnessing facial recognition techniques to address a broad spectrum of ecological questions that require individual ID in "unmarked" species.