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Is Facial Recognition Biased at Near-Infrared Spectrum As Well?
Interestingly, experimental results suggest equitable face recognition performance across gender and race at the NIR spectrum.
Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults
A number of studies suggest bias of the face biometrics, i. e., face recognition and soft-biometric estimation methods, across gender, race, and age groups.
Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset
To this aim, VISOB $2. 0$ dataset, along with its gender annotations, is used for the fairness analysis of ocular biometrics methods based on ResNet-50, MobileNet-V2 and lightCNN-29 models.
BWCFace: Open-set Face Recognition using Body-worn Camera
To this aim, the contribution of this work is two-fold: (1) collection of a dataset called BWCFace consisting of a total of 178K facial images of 132 subjects captured using the body-worn camera in in-door and daylight conditions, and (2) open-set evaluation of the latest deep-learning-based Convolutional Neural Network (CNN) architectures combined with five different loss functions for face identification, on the collected dataset.
