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pro vyhledávání: '"Bowyer, Kevin W."'
This paper studies how to synthesize face images of non-existent persons, to create a dataset that allows effective training of face recognition (FR) models. Two important goals are (1) the ability to generate a large number of distinct identities (i
Externí odkaz:
http://arxiv.org/abs/2409.02979
Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairsty
Externí odkaz:
http://arxiv.org/abs/2405.20062
Autor:
Wu, Haiyu, Tian, Sicong, Bhatta, Aman, Gutierrez, Jacob, Bezold, Grace, Argueta, Genesis, Ricanek Jr., Karl, King, Michael C., Bowyer, Kevin W.
Face Recognition models are commonly trained with web-scraped datasets containing millions of images and evaluated on test sets emphasizing pose, age and mixed attributes. With train and test sets both assembled from web-scraped images, it is critica
Externí odkaz:
http://arxiv.org/abs/2405.15965
A fundamental tenet of pattern recognition is that overlap between training and testing sets causes an optimistic accuracy estimate. Deep CNNs for face recognition are trained for N-way classification of the identities in the training set. Accuracy i
Externí odkaz:
http://arxiv.org/abs/2405.09403
Autor:
Khan, Siamul Karim, Tinsley, Patrick, Mitcheff, Mahsa, Flynn, Patrick, Bowyer, Kevin W., Czajka, Adam
Synthesis of same-identity biometric iris images, both for existing and non-existing identities while preserving the identity across a wide range of pupil sizes, is complex due to intricate iris muscle constriction mechanism, requiring a precise mode
Externí odkaz:
http://arxiv.org/abs/2312.12028
The first layer of a deep CNN backbone applies filters to an image to extract the basic features available to later layers. During training, some filters may go inactive, mean ing all weights in the filter approach zero. An inactive fil ter in the fi
Externí odkaz:
http://arxiv.org/abs/2312.00072
Ensuring logical consistency in predictions is a crucial yet overlooked aspect in multi-attribute classification. We explore the potential reasons for this oversight and introduce two pressing challenges to the field: 1) How can we ensure that a mode
Externí odkaz:
http://arxiv.org/abs/2311.11208
State-of-the-art deep CNN face matchers are typically created using extensive training sets of color face images. Our study reveals that such matchers attain virtually identical accuracy when trained on either grayscale or color versions of the train
Externí odkaz:
http://arxiv.org/abs/2309.05180
Most studies to date that have examined demographic variations in face recognition accuracy have analyzed 1-to-1 matching accuracy, using images that could be described as "government ID quality". This paper analyzes the accuracy of 1-to-many facial
Externí odkaz:
http://arxiv.org/abs/2309.04447
Autor:
Wu, Haiyu, Bowyer, Kevin W.
The issue of demographic disparities in face recognition accuracy has attracted increasing attention in recent years. Various face image datasets have been proposed as 'fair' or 'balanced' to assess the accuracy of face recognition algorithms across
Externí odkaz:
http://arxiv.org/abs/2304.09818