Neural Network Facial Image BMI Prediction
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Solution Overview
Problem
Existing methods lack accuracy, efficiency, and convenience in predicting physiological parameters such as Body Mass Index (BMI) and Basal Metabolic Rate (BMR) from facial images, as they often require high-quality images and are not reliable for various health condition assessments.
Innovation Solution
A deep learning-based system utilizing a Network-in-Network (NiN) model processes facial images to predict weight and height, which are then used to calculate BMI, incorporating image pre-processing to filter out unsuitable images and a large dataset for training the neural network to minimize prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to predict physiological parameters from facial images, then the system can operate with simpler architecture, but the accuracy and reliability of predictions deteriorate
Solution Approach 1:
The patent replaces traditional mechanical/image-processing-based measurement systems with a deep learning neural network model. The neural network automatically learns complex patterns from facial images to predict physiological parameters such as BMI, age, and gender, achieving high accuracy without requiring complex manual processing systems or specialized hardware.
Solution Approach 2:
The patent transforms the approach by changing from traditional image processing parameters to deep learning model parameters. The neural network processes images through multiple layers of convolutional operations, extracting hierarchical features that enable accurate prediction of physiological parameters, thereby improving measurement precision through parameter transformation rather than system complexity.
2Measurement precision
If high-quality images are required for accurate prediction, then the measurement precision improves, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically processing and evaluating image quality during the prediction process. The neural network model inherently handles variations in image quality through its learned representations, automatically adapting to different lighting conditions, angles, and resolutions without requiring users to manually adjust or select optimal images, thereby maintaining ease of operation while ensuring prediction accuracy.
Solution Approach 2:
The patent applies preliminary action through image pre-processing steps that automatically prepare input images before they are fed to the neural network. The system performs automatic image enhancement, normalization, and quality assessment in advance, ensuring that even images taken under suboptimal conditions are transformed into suitable input for accurate prediction, thus maintaining user convenience without sacrificing precision.
3Reliability
If a large dataset is used for training the neural network, then the reliability of predictions improves, but the loss of time in data collection and processing increases
Solution Approach 1:
The patent utilizes copying by leveraging existing large-scale image datasets and pre-trained neural network models. Instead of collecting entirely new data from scratch, the system builds upon established data resources and transfer learning frameworks, thereby achieving high prediction reliability while significantly reducing the time required for data collection and model training through utilization of pre-existing computational resources and data copies.
Data Source
AI summary
System and method for determining physiological parameters of a person are disclosed. A physiological parameter may be obtained by analyzing a facial image of a person, and determining, from the facial image, a physiological parameter of the person by processing the facial image with a data processor. A neural network model such as regression deep learning convolutional neural network is used to predict the physiological parameter. An image processor screens out images which can't be recognized as facial images and adjust facial images to frontal facial images for predicting of physiological parameters.