Abstract: This disclosure provides a highly scalable training data preparation pipeline for data cleaning and augmentation with the aim of extracting the most meaningful information while keeping the noise level low, as well as a highly efficient distributed trainer for the deep neural networks suitable for facial recognition. The goal is to train deeper and larger neural networks with larger and higher quality facial image datasets iteratively and frequently without incurring prohibitive costs and drastic delays.
Abstract: This disclosure provides a highly scalable training data preparation pipeline for data cleaning and augmentation with the aim of extracting the most meaningful information while keeping the noise level low, as well as a highly efficient distributed trainer for the deep neural networks suitable for facial recognition. The goal is to train deeper and larger neural networks with larger and higher quality facial image datasets iteratively and frequently without incurring prohibitive costs and drastic delays.
Abstract: This disclosure provides methods for providing information about a person based on facial recognition and various applications thereof, including face-based check-in, face-based personal identification, face-based identification verification, face-based background checks, facial data collaborative network, correlative face search, and personal face-based identification. The disclosed methods are able to provide accurate information about a person in a real-time manner.
Abstract: This disclosure provides methods for providing information about a person based on facial recognition and various applications thereof, including face-based check-in, face-based personal identification, face-based identification verification, face-based background checks, facial data collaborative network, correlative face search, and personal face-based identification. The disclosed methods are able to provide accurate information about a person in a real-time manner.