Book ChapterDOI
Exploring Bias in Primate Face Detection and Recognition
Sanchit Sinha,Mohit Agarwal,Mayank Vatsa,Richa Singh,Saket Anand +4 more
- Vol. 11129, pp 541-555
TLDR
Experimental results on a primate dataset of over 80 identities show the effect of bias in this research problem, and whether the knowledge of human faces and recent methods learned from human face detection and recognition can be extended to primate faces.Abstract:
Deforestation and loss of habitat have resulted in rapid decline of certain species of primates in forests. On the other hand, uncontrolled growth of a few species of primates in urban areas has led to safety issues and nuisance for the local residents. Hence, identifying individual primates has become the need of the hour - not only for conservation and effective mitigation in the wild but also in zoological parks and wildlife sanctuaries. Primates and human faces share a lot of common features like position and shape of eyes, nose and mouth. It is worth exploring whether the knowledge of human faces and recent methods learned from human face detection and recognition can be extended to primate faces. However, similar challenges relating to bias in human faces will also occur in primates. The quality and orientation of primate images along with different species of primates - ranging from monkeys to gorillas and chimpanzees will contribute to bias in effective detection and recognition. Experimental results on a primate dataset of over 80 identities show the effect of bias in this research problem.read more
Citations
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Journal ArticleDOI
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems
TL;DR: This work surveys research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains, and argues for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods.
Posted Content
Count, Crop and Recognise: Fine-Grained Recognition in the Wild
TL;DR: A 'Count, Crop and Recognise' (CCR) multi-stage recognition process for frame level labelling for chimpanzee recognition in the wild is introduced and a high-granularity visualisation technique is applied to further understand the learned CNN features for the recognition of chimpanzee individuals.
Journal ArticleDOI
A deep transfer learning model for head pose estimation in rhesus macaques during cognitive tasks: towards a nonrestraint noninvasive 3Rs approach
TL;DR: In this article , a free-to-use deep transfer learning model was proposed for non-invasive head pose estimation in unrestrained Macaca mulatta taking part in cognitive experiments.
Proceedings ArticleDOI
Triplet Transform Learning for Automated Primate Face Recognition
TL;DR: A novel Triplet Transform Learning (TTL) model for learning discriminative representations of primate faces is proposed, where it outperforms the existing approaches and attains state-of-the-art performance on the primates database.
Proceedings ArticleDOI
Misclassifications of Contact Lens Iris PAD Algorithms: Is it Gender Bias or Environmental Conditions?
TL;DR: In this article , the authors presented a rigorous study on gender bias in iris presentation attack detection algorithms using a large-scale and gender-balanced database, which can help in building future presentation attacks detection algorithms with the aim of fair treatment of each demography.
References
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Proceedings ArticleDOI
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