1 code implementation • 25 Jan 2024 • Torsten Schlett, Christian Rathgeb, Juan Tapia, Christoph Busch
Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra- and inter-subject duplicate sets.
1 code implementation • 23 Mar 2023 • Torsten Schlett, Christian Rathgeb, Juan Tapia, Christoph Busch
Additionally, a discard fraction limit or range must be selected to compute pAUC values, which can then be used to quantitatively rank quality assessment algorithms.
no code implementations • 24 Feb 2023 • Torsten Schlett, Sebastian Schachner, Christian Rathgeb, Juan Tapia, Christoph Busch
This work investigates the effect of lossy image compression on a state-of-the-art face recognition model, and on multiple face image quality assessment models.
no code implementations • 2 Sep 2020 • Torsten Schlett, Christian Rathgeb, Olaf Henniger, Javier Galbally, Julian Fierrez, Christoph Busch
The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors.
no code implementations • 19 Jun 2020 • Torsten Schlett, Christian Rathgeb, Christoph Busch
All tested enhancer types exclusively use depth data as input, which differs from methods that enhance depth based on additional input data such as visible light color images.