Nvidia corporation (20240265254). NEURAL NETWORK BASED FACIAL ANALYSIS USING FACIAL LANDMARKS AND ASSOCIATED CONFIDENCE VALUES simplified abstract

NEURAL NETWORK BASED FACIAL ANALYSIS USING FACIAL LANDMARKS AND ASSOCIATED CONFIDENCE VALUES

Organization Name

nvidia corporation

Inventor(s)

Nuri Murat Arar of Zurich (CH)

Niranjan Avadhanam of Saratoga CA (US)

Nishant Puri of San Francisco CA (US)

Shagan Sah of Santa Clara CA (US)

Rajath Shetty of Santa Clara CA (US)

Sujay Yadawadkar of Santa Clara CA (US)

Pavlo Molchanov of Mountain View CA (US)

NEURAL NETWORK BASED FACIAL ANALYSIS USING FACIAL LANDMARKS AND ASSOCIATED CONFIDENCE VALUES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240265254 titled 'NEURAL NETWORK BASED FACIAL ANALYSIS USING FACIAL LANDMARKS AND ASSOCIATED CONFIDENCE VALUES

Simplified Explanation:

This patent application describes systems and methods for accurately determining subject characteristics from an image using machine learning models to identify facial landmarks and associated confidence values.

• Machine learning models analyze an image of a subject to identify facial landmarks and their associated confidence values, indicating the uncertainty in the position of each landmark due to occlusion.
• The identified facial landmarks and confidence values can be used as input for additional machine learning models to determine facial analysis quantities such as gaze direction, head pose, drowsiness state, cognitive load, or distraction state.

Key Features and Innovation:

• Accurate determination of subject characteristics from images using machine learning models.
• Identification of facial landmarks and associated confidence values to account for occlusion and uncertainty.
• Ability to analyze facial landmarks and confidence values to determine various facial analysis quantities.

Potential Applications:

• Biometric security systems
• Human-computer interaction interfaces
• Healthcare applications for monitoring patient conditions
• Automotive safety systems

Problems Solved:

• Uncertainty in determining subject characteristics from images
• Inaccurate facial analysis due to occlusion
• Lack of robust methods for analyzing facial features in images

Benefits:

• Improved accuracy in determining subject characteristics
• Enhanced facial analysis capabilities
• Increased reliability in identifying facial landmarks

Commercial Applications:

Facial recognition technology for security systems and access control Human-computer interaction interfaces for virtual reality and augmented reality applications Healthcare monitoring systems for patient care and diagnostics Automotive safety systems for driver monitoring and alertness detection

Questions about Facial Analysis Technology:

1. How does this technology improve upon existing methods for facial analysis? 2. What are the potential privacy concerns associated with using facial analysis technology in various applications?

Original Abstract Submitted

systems and methods for more accurate and robust determination of subject characteristics from an image of the subject. one or more machine learning models receive as input an image of a subject, and output both facial landmarks and associated confidence values. confidence values represent the degrees to which portions of the subject's face corresponding to those landmarks are occluded, i.e., the amount of uncertainty in the position of each landmark location. these landmark points and their associated confidence values, and/or associated information, may then be input to another set of one or more machine learning models which may output any facial analysis quantity or quantities, such as the subject's gaze direction, head pose, drowsiness state, cognitive load, or distraction state.