Invention Grant
- Patent Title: Meta-learning for facial recognition
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Application No.: US16036757Application Date: 2018-07-16
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Publication No.: US10832036B2Publication Date: 2020-11-10
- Inventor: Haoxiang Li , Zhe Lin , Muhammad Abdullah Jamal
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Agency: Shook, Hardy & Bacon L.L.P.
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06N3/08

Abstract:
Methods and systems are provided for generating a facial recognition system. A facial recognition system can be implemented using a meta-model based on a trained neural network. A neural network can be trained as multiple classifiers that identify individuals using a small number of images of the individual's face. A meta-model can learn from the neural networks to be capable to identify an individual based on a small number of images. In this way, the facial recognition system uses the meta-model that learns from the neural network trained to identify an individual based on a small number of images. Such a facial recognition system is tested to determine any misidentification for fine-tuning the system. A facial recognition system implemented using such a meta-model is capable of adapting the model to learn identities entered into the system using only a small number of images to enroll an identity into the system.
Public/Granted literature
- US20200019758A1 META-LEARNING FOR FACIAL RECOGNITION Public/Granted day:2020-01-16
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