Face Age Estimation Using Generation-Specific Likelihoods
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Estimating the exact age from a face image is challenging due to the difficulty in accurately interpreting facial features, similar to humans' inability to estimate others' age accurately.
Innovation Solution
An information processing apparatus that extracts features from face images, estimates age likelihoods using generation-specific classifiers, selects samples based on these likelihoods, and determines an estimated age with an error range by analyzing dictionary samples and their distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generation-specific classifiers are used to estimate age from face images, then age estimation capability is provided, but measurement precision is insufficient due to the difficulty in accurately interpreting facial features
Solution Approach 1:
The patent introduces generation-specific classifiers as intermediary components that bridge the gap between raw facial features and age estimation. These classifiers act as mediators that transform complex facial feature data into generation likelihoods, which are then combined to produce more accurate age estimates. The classifiers serve as intermediate processing layers that enhance the overall measurement precision of the age estimation system.
Solution Approach 2:
The patent segments the age estimation process into multiple independent generation-specific classifiers, each responsible for estimating the likelihood of a specific generation (e.g., 20s, 30s, 40s). This segmentation allows each classifier to specialize in recognizing features characteristic of its target generation, thereby improving overall estimation accuracy. The final age estimate is derived by combining the outputs of these segmented classifiers.
2Measurement precision
If multiple generation-specific classifiers are employed to improve age estimation accuracy, then measurement precision increases, but device complexity increases due to multiple classifiers and sample management
Solution Approach 1:
The patent implements a universal sample management mechanism that serves multiple generation-specific classifiers. The storage unit containing generation-specific samples and the selection unit that retrieves appropriate samples function as universal components supporting all classifiers. This multi-functionality reduces overall system complexity by avoiding redundant sample storage and management structures for each classifier.
Solution Approach 2:
The patent merges the sample storage and selection functionality into unified components that serve all generation-specific classifiers. Instead of maintaining separate sample databases and selection mechanisms for each classifier, the system combines these resources into shared infrastructure, thereby reducing device complexity while maintaining the benefits of multiple specialized classifiers.
3Loss of information
If the system provides detailed age estimation results, then information completeness improves, but reliability decreases due to potential estimation errors
Solution Approach 1:
The patent changes the output parameter from a single deterministic age value to a probability distribution across multiple generations. By providing generation likelihoods rather than a fixed age estimate, the system maintains complete age information while expressing uncertainty through probabilistic parameters. This parameter transformation allows the system to convey both information completeness and reliability simultaneously.
Solution Approach 2:
The patent incorporates feedback mechanisms where the selection unit evaluates the confidence levels of classification results and adjusts sample selection accordingly. When classification confidence is low, the system can indicate higher uncertainty in the age estimation, providing feedback about reliability to the user. This feedback loop maintains information completeness while honestly representing estimation reliability.
Data Source
AI summary
An information processing apparatus includes an extraction unit that extracts a feature from an image including a face, a first estimation unit that estimates a likelihood of the face with respect to each generation based on the feature, a storage unit that stores a plurality of samples, the plurality of samples each including a generation-specific combination of likelihoods and a correct age as a pair, a selection unit that selects a sample from the storage unit based on a combination of likelihoods estimated by the first estimation unit, and a second estimation unit that estimates an estimated age of the face and an error range thereof based on the sample selected by the selection unit.