With the advance of sensor technology, existing antispoofing systems can be vulnerable to emerging high-quality spoof mediums. One way to make the system robust to these attacks is to collect new high-quality databases. In response to this need, we collect a new face anti-spoofing database named Spoof in the Wild ...Continue reading
Keywords: Face antispoofing
DR-GAN face frontalization demo can be found here.
Tensorflow pre-trained model can be download here.
If you use these results, please cite to the papers:Continue reading
Dense Face Alignment implementation may be downloaded from here.
Pose-Invariant Face Alignment with a Single CNN implementation may be downloaded from here.
PIFA implementation may be downloaded from here.
The part of AFLW database used for training and testing can be found from here.
If you use PIFA code, please ...Continue reading
Keywords: Face alignment
We collect a multi-modality plant imagery database named “MSU-PID” including two types of plants: Arabidopsis and bean. It is captured using four types of imaging sensors:fluorescence, infrared(IR), RGB color, and depth. The imaging setup and the variety of manual labels allow MSU-PID to be used for a diverse ...Continue reading
This dataset is released in two different forms. Acoustics: 45 subjects from phase 1. Visual: Full dataset. Typing Behavior Dataset may be downloaded from here.
We collect a first-of-its kind keystroke database in two phases. Phase 1 includes 56 subjects typing multiple same day, fixed and free text, sessions. It ...Continue reading
Keywords: Typing behavior