Shared Convolutional Neural Network for Age Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional machine learning methods for age estimation of face images suffer from low accuracy due to poor robustness against noise, background, and lighting variations, leading to significant age differences in estimates from the same person's images taken in different scenes.
Innovation Solution
The method involves training a gender model and an age model with shared convolution layers, where the weights of these layers remain unchanged during age model training, allowing gender information to supervise age estimation, thereby improving accuracy by eliminating gender-related inaccuracies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used for age estimation, then the implementation is simple, but the accuracy of age estimation is low due to poor robustness against noise, background, and lighting variations
Solution Approach 1:
The patent combines gender classification and age estimation into a single unified neural network model. The model simultaneously performs both tasks by sharing common feature extraction layers, allowing the system to leverage gender information to improve age estimation accuracy while maintaining robustness against noise and background variations.
Solution Approach 2:
The neural network model is designed with multi-functionality to perform both gender classification and age estimation. By making the model universal for both tasks, it learns robust facial features that are useful for both functions, improving overall performance and reliability in varying conditions.
2Measurement precision
If separate models are trained for gender and age independently, then the training process is simple, but the age estimation accuracy deteriorates due to gender-related inaccuracies
Solution Approach 1:
The patent merges gender classification and age estimation into a single neural network model with shared feature extraction layers. This integration allows the model to learn gender-invariant features for age estimation while still performing accurate gender classification, thereby improving age estimation accuracy without excessive complexity increase.
Solution Approach 2:
The neural network model is segmented into shared feature extraction layers and task-specific output layers. The shared layers learn common facial features useful for both gender and age, while separate output layers handle each specific task, balancing model complexity with performance improvement.
Data Source
AI summary
Provided in embodiments of the present application are a method and device for age estimation. The method comprises: performing gender training with respect to a gender model on the basis of facial image samples so as to allow the gender model to converge, where the gender model comprises at least two convolution layers; performing age training with respect to an age model on the basis of the facial samples so as to allow the age model to converge, where the age model comprises the at least two convolution layers, the converged age model comprises the weights of the at least two convolution layers, and the weights of the at least two convolution layers that the converged gender model comprises; and performing age estimation with respect to an inputted facial image on the basis of the converged age model. The technical solution provided in the embodiments of the present application eliminates the problem of inaccurate age estimation as a result of gender differences of facial images, thus increasing the accuracy of age estimation.