no code implementations • 11 Oct 2021 • Ali Almadan, Ajita Rattani
A number of studies have demonstrated the efficacy of deep learning convolutional neural network (CNN) models for ocular-based user recognition in mobile devices.
no code implementations • 4 Oct 2021 • Anoop Krishnan, Ali Almadan, Ajita Rattani
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.
no code implementations • 7 Apr 2021 • Ali Almadan, Ajita Rattani
Face recognition technology in body-worn cameras is used for surveillance, situational awareness, and keeping the officer safe.
no code implementations • 17 Nov 2020 • Anoop Krishnan, Ali Almadan, Ajita Rattani
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.
no code implementations • 24 Sep 2020 • Anoop Krishnan, Ali Almadan, Ajita Rattani
For instance, for all the algorithms used, Black females (Black race in general) always obtained the least accuracy rates.
no code implementations • 24 Sep 2020 • Ali Almadan, Anoop Krishnan, Ajita Rattani
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.