Facial Age Identification Sample Balancing
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Solution Overview
Problem
Conventional facial age identification methods face inaccuracies due to uneven numbers of positive and negative samples, leading to high error rates in classification functions.
Innovation Solution
The method involves acquiring facial images and assigning them into age groups, selecting positive samples from older age groups and negative samples from younger age groups, and performing training using these samples to determine classification functions, ensuring approximately even sample numbers for accurate age estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional facial age identification methods are used with multiple image samples, then facial age can be identified, but the number of negative samples is over ten times the number of positive samples leading to high error rates
Solution Approach 1:
The patent changes the parameter of sample selection criteria by introducing age-group-based classification. Instead of randomly selecting samples, the system selects positive samples from age groups older than the target and negative samples from age groups younger than the target, ensuring balanced sample distribution and improving classification accuracy
Solution Approach 2:
The patent segments the sample selection process into distinct age groups. By dividing the population into multiple age groups and selecting samples based on their relative position to the target age group, the system creates balanced positive and negative sample sets, resolving the imbalance problem
Data Source
AI summary
Systems and methods are provided for acquiring classification functions for facial age identification. For example, a plurality of facial images associated with different ages are acquired; the facial images are assigned into a plurality of facial image collections based on at least information associated with a plurality of first age groups; for a first age group, one or more first facial image collections associated with one or more second age groups older than the first age group are acquired as positive samples; one or more second facial image collections associated with one or more third age groups younger than the first age group are acquired as negative samples; and training is performed based on at least information associated with the first positive samples and the negative samples to determine one or more classification functions for the first age groups.