HIGH-QUALITY TRAINING DATA PREPARATION FOR HIGH-PERFORMANCE FACE RECOGNITION SYSTEMS

摘要

Embodiments described herein provide various examples of a face-image training data preparation system for performing large-scale face-image training data acquisition, pre-processing, cleaning, balancing, and post-processing. The disclosed training data preparation system can collect a very large set of loosely-labeled images of different people from the public domain, and then generate a raw training dataset including a set of incorrectly-labeled face images. The disclosed training data preparation system can then perform cleaning and balancing operations on the raw training dataset to generate a high-quality face-image training dataset free of the incorrectly-labeled face images. The processed high-quality face-image training dataset can be subsequently used to train deep-neural-network-based face recognition systems to achieve high performance in various face recognition applications. Compared to conventional face recognition systems and techniques, the disclosed training data preparation system and technique provide a fully-automatic, highly-deterministic and high-quality training data preparation procedure which does not rely heavily on assumptions.