Millimeter-Wave Radar Facial Identification Using CNN Image Conversion

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Solution Overview

Problem

Current facial identification methods using image-based sensors face challenges in privacy and performance in dark environments, and existing radar sensors are limited in their ability to accurately classify human faces due to variations in reflection characteristics.

Innovation Solution

A method utilizing a millimeter wave frequency-modulated continuous-wave radar sensor to transmit and receive radar signals, converting the data into an image format suitable for a convolutional neural network for facial identification, with a system comprising transmitting and receiving antennas and a digital signal processor to perform classification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If image-based sensors are used for facial identification, then identification capability is achieved, but privacy is compromised and performance deteriorates in dark environments

Engineering Contradiction:

Improvefacial identification accuracyVSAvoidprivacy loss and dark environment limitation

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent replaces image-based optical sensors with millimeter-wave radar sensors that use electromagnetic waves. This substitution eliminates the need for visible light (solving dark environment performance issues) and does not capture visual images (solving privacy concerns), while still enabling facial identification through reflection characteristic analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Object-affected harmful factors

If traditional radar sensors are used to detect faces, then privacy protection is achieved, but classification accuracy is insufficient due to reflection characteristic variations

Engineering Contradiction:

Improveprivacy protectionVSAvoidfacial classification accuracy

Core Design Contradiction:

Object-affected harmful factorsVSMeasurement precision

Solution Approach 1:

The patent changes the operational parameters of the radar sensor to millimeter-wave frequency (60 GHz band with 6 GHz bandwidth), which provides higher resolution and better discrimination of facial reflection characteristics compared to traditional radar frequencies. This parameter change enables accurate classification while maintaining privacy protection.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent transforms the radar data from traditional distance-velocity information into a two-dimensional image representation that captures spatial distribution of reflection characteristics across the face. This dimensional transformation enables the application of image processing techniques and neural networks to improve classification accuracy.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Measurement precision

If millimeter-wave radar with wide bandwidth is used, then facial identification accuracy is improved, but device complexity and data processing requirements increase

Engineering Contradiction:

Improvefacial identification accuracyVSAvoidsignal processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies Fast Fourier Transform (FFT) processing to convert the raw radar signal data into frequency domain representation and then into image form before classification. This preliminary transformation simplifies the subsequent classification task by organizing the data into a more interpretable format that highlights facial features.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediate image representation as a mediator between the raw radar signals and the classification algorithm. This image form serves as a bridge that translates complex radar data into a format suitable for convolutional neural network processing, reducing the complexity of direct signal classification.

Inventive Principle:

Principle #24Intermediary (Mediator)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Achieves high accuracy in facial identification, with a classification performance greater than 98% and minimal impact from subjects wearing cotton masks, while providing privacy protection and effective performance in dark environments.

Implementation Method 1

transmitting a radar signal onto faces of subjects using the millimeter wave radar sensor; receiving and accumulating reflected radar data

Methodology Applied

Scientific EffectRadar: Radar

Implementation Method 2

reflected radar data is received from channels of multiple receiving antennas

Methodology Applied

Scientific EffectElectromagnetic reflection: Reflection

Data Source

PatentUS12183119B2Face identification using millimeter-wave radar sensor data

Publication Date: 2024.12.31 BITSENSING INC

AI summary

Facial identification of subjects using a millimeter wave radar sensor, including: transmitting a radar signal onto faces of subjects using the millimeter wave radar sensor; receiving and accumulating reflected radar data, wherein the reflected radar data is received from channels of multiple receiving antennas; converting the reflected radar data into an image form suitable as an input to a convolutional neural network; and performing the facial identification on the image form using a classifier trained by the convolutional neural network.