GSoC’20 RoboComp project: Human recognition (identification) using multi-modal perception system
11th June 2020
Benchmarking methods for face analysis
Facial analysis is used to automatically label an individualin by retrieving it’s face features. It can be divided into 2 steps:
In each frame of the video we need to find the bounding box for each person’s face. To choose the technique to integrate, we compare their performance and computation time.
Top performers on UTK dataset
Based on these results, we have choosen Mt-cnn as the face-detection model.
Face recognition is a method of identifying or verifying the identity of an individual using their face.
Top performers on LFW dataset
2 versions of ArcFace are benchmarked. - Using Resnet-100 as the backbone - Using MobileFacenet as the backbone
Based on these results, we have choosen Arcface as the face-recognition model.
Current updates on the project
Please follow the following link for current evaluation of different methods of face recognition and gait recognition.