We have developed an end-to-end system for unconstrained face verification and search. Our system allows us to build robust, succinct face representations of many faces of the same identity that are amenable to accurate and efficient verification, search, clustering and indexing. Our system has been tested extensively both with public and sequestered datasets and exhibits state of the art performance. In building this system we have made advances in face detection, keypoint detection and loss functions for the training of deep convolutional neural networks.

References

1. Rajeev Ranjan, Swami Sankaranarayanan, Ankan Bansal, Navaneeth Bodla, Jun-Cheng Chen, Vishal M Patel, Carlos D Castillo and Rama Chellappa. “Deep learning for understanding faces: Machines may be just as good, or better, than humans”, Journal IEEE Signal Processing Magazine, 2018
2. Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo and Rama Chellappa. “An all-in-one convolutional neural network for face analysis.” 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG), 2017.
3. Kumar, Amit, Azadeh Alavi, and Rama Chellappa. “Kepler: Keypoint and pose estimation of unconstrained faces by learning efficient h-cnn regressors.” 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, 2017.
4. Ranjan, Rajeev, Vishal M. Patel, and Rama Chellappa. “A deep pyramid deformable part model for face detection.” 7th international conference on biometrics theory, applications and systems (BTAS). IEEE, 2015.