Detection and Identification of Chimpanzee Faces in the Wild

Loos, Alexander; Ernst, Andreas T.

doi:10.1109/ism.2012.30

Cited by 7 publications

(10 citation statements)

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“…Based on the assumption that humans and chimpanzees share similar properties of the face, we proposed to use the face detection and recognition technology for identification of great apes in our previous work [5][6][7][8][9]. In this paper we successfully combined face detection, face alignment, and face recognition to a complete identification system for chimpanzee faces in real-world environments.…”

Section: Discussionmentioning

confidence: 99%

“…We summarize and extend our previous work from [5][6][7][8][9] on face detection and individual identification of African great apes for wildlife monitoring and present an automated framework to detect and subsequently identify free-living as well as captured chimpanzee individuals in uncontrolled environments.…”

Section: Introductionmentioning

confidence: 86%

“…Another drawback is our assumption that faces and facial feature points were already detected properly for alignment and recognition. We overcame the latter issue by combining face and facial feature detection as well as face recognition and presented an automated identification system for chimpanzees in [7]. However, we only used simple pixel information in the recognition part of the proposed system.…”

Section: Own Workmentioning

confidence: 99%

“…Experiment 1: influence of visual features and feature space transformation In the first experiment we want to address the question if Gabor features (GABOR) or pixel based features (PIXEL), used in our previous work [5,7], are better suited for face recognition of great apes. Furthermore, we evaluate and compare three different feature space transformation techniques: random projection (RAND) [24], PCA [21], and LPP [23].…”

Section: Face Identificationmentioning

confidence: 99%

“…We extended our approaches from [6] and [7] to improve the system's robustness against pose variations, difficult lighting conditions, and partial occlusions [8]. However, in this paper we present a complete system for chimpanzee photo identification including face detection, face alignment, and face recognition.…”

Section: Introductionmentioning

confidence: 99%

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An automated chimpanzee identification system using face detection and recognition

Loos

Ernst

2013

J Image Video Proc

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Due to the ongoing biodiversity crisis, many species including great apes like chimpanzees are on the brink of extinction. Consequently, there is an urgent need to protect the remaining populations of threatened species. To overcome the catastrophic decline of biodiversity, biologists and gamekeepers recently started to use remote cameras and recording devices for wildlife monitoring in order to estimate the size of remaining populations. However, the manual analysis of the resulting image and video material is extremely tedious, time consuming, and cost intensive. To overcome the burden of time-consuming routine work, we have recently started to develop computer vision algorithms for automated chimpanzee detection and identification of individuals. Based on the assumption that humans and great apes share similar properties of the face, we proposed to adapt and extend face detection and recognition algorithms, originally developed to recognize humans, for chimpanzee identification. In this paper we do not only summarize our earlier work in the field, we also extend our previous approaches towards a more robust system which is less prone to difficult lighting situations, various poses, and expressions as well as partial occlusion by branches, leafs, or other individuals. To overcome the limitations of our previous work, we combine holistic global features and locally extracted descriptors using a decision fusion scheme. We present an automated framework for photo identification of chimpanzees including face detection, face alignment, and face recognition. We thoroughly evaluate our proposed algorithms on two datasets of captive and free-living chimpanzee individuals which were annotated by experts. In three experiments we show that the presented framework outperforms previous approaches in the field of great ape identification and achieves promising results. Therefore, our system can be used by biologists, researchers, and gamekeepers to estimate population sizes faster and more precisely than the current frameworks. Thus, the proposed framework for chimpanzee identification has the potential to open up new venues in efficient wildlife monitoring and can help researches to develop innovative protection schemes in the future.

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Section: Discussionmentioning

confidence: 99%