Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates

Freytag, Andreas; Rodner, Erik; Simon, Marcel; Loos, Alexander; Kühl, Hjalmar S.; Denzler, Joachim

doi:10.1007/978-3-319-45886-1_5

Cited by 68 publications

(72 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent progress in the emerging field of animal biometrics [36], in particular in ape facial recognition [47,12,46,16] promises to overcome some of these obstacles and make broad-scale, real-world applications a realistic prospect.…”

Section: Monitoring In Ecology Todaymentioning

confidence: 99%

“…In contrast to purely fine-grained category recognition, we are interested in the identification of individual animals [16,26] (or instances) of a single species (or category) rather than highly similar categories. Whilst animal biometrics [36] may operate on a wide variety of entities to achieve identification, our technical focus of the review will be solely on techniques applicable to facial identification.…”

Section: Automated Detection and Identificationmentioning

confidence: 99%

“…The VGG-faces architecture by Parkhi et al continued that trend of large sample utilisation employing 2.6M face images for training [56]. While these advanced approaches show promising results in human facial identification, they are not necessarily transferable to great apes, where general models were shown to work more reliably [16].…”

Section: Automated Detection and Identificationmentioning

confidence: 99%

“…Another system for chimpanzee identification, including attribute estimation for gender and age, was proposed by Freytag et al [16]. Since this system serves as the base template for our gorilla identification approach it is now reviewed in detail.…”

Section: Automated Detection and Identificationmentioning

confidence: 99%

See 3 more Smart Citations

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Brust

Burghardt

Groenenberg

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

Self Cite

View full text Add to dashboard Cite

In this paper we report on the context and evaluation of a system for an automatic interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as captured in facial fieldVisual data acquisition in the field often captures sufficient information to establish encounters with individual gorillas. However, relevant information is locked within the pixel patterns measured, usually requiring expert knowledge and time-consuming efforts for identification. Computer vision can help to extract gorilla identities by performing automated species detection, followed by individual facial identification. We show that standard deep learning models combined with a traditional SVM classifier can be used for this task. To assist encounter processing, predictions can be presented graphically with known population information as shown.

show abstract

Section: Monitoring In Ecology Todaymentioning

confidence: 99%

Section: Automated Detection and Identificationmentioning

confidence: 99%

Section: Automated Detection and Identificationmentioning

confidence: 99%

Section: Automated Detection and Identificationmentioning

confidence: 99%

See 2 more Smart Citations

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Brust

Burghardt

Groenenberg

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

Self Cite

View full text Add to dashboard Cite

show abstract

“…A method based on local binary patterns [21] has been shown to be relatively robust to changes in light intensity. Most recently deep learning techniques have been applied to face recognition with a high level of success [22,23]. In images, numerous approaches are proposed for face detection.…”

Section: Introductionmentioning

confidence: 99%

Boosting Based Implementationof Biometric Authentication in IoT

Thilagavathi

Suthendran

2018

JCSM

View full text Add to dashboard Cite

In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.

show abstract

A study on giant panda recognition based on images of a large proportion of captive pandas

Chen

Swarup

Matkowski

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

show abstract

Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates

Cited by 68 publications

References 30 publications

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Boosting Based Implementationof Biometric Authentication in IoT

A study on giant panda recognition based on images of a large proportion of captive pandas

Contact Info

Product

Resources

About