Age Gender Estimation via Head Pose Alignment
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Solution Overview
Problem
Current age and gender estimation methods face challenges due to insufficient datasets, variations in expression, pose, and illumination, and require significant computational resources, making them unsuitable for real-world applications with constrained computational capabilities.
Innovation Solution
A computationally efficient framework using lightweight deep learning neural networks, specifically designed for age and gender estimation, which includes a head pose estimation module, utilizes a combination of datasets like Asian Face Age Dataset, UTKFace, and IMDB-WIKI for training, and employs structural adjustments such as dropout deletion and replacing CNN backbones with ResNet50 and EfficientNetB4 to balance accuracy and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep learning models are used for age and gender estimation, then accuracy can be improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the deep learning model into modular components including a head pose estimation module and a facial attribute estimation module. This segmentation allows the system to process only relevant features for age and gender estimation, reducing overall computational resource consumption while maintaining accuracy.
Solution Approach 2:
The patent extracts and removes unnecessary computational components from traditional deep learning models. By taking out non-essential layers and operations that do not contribute to age and gender estimation, the model achieves comparable accuracy with significantly reduced computational resource requirements.
2Measurement precision
If comprehensive datasets with multiple variations are used for training, then estimation accuracy improves, but dataset complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by performing head pose estimation and image alignment before the main age and gender estimation process. This preprocessing step standardizes the input data by correcting for head pose variations, allowing the model to achieve high accuracy without requiring excessively complex datasets that cover all possible pose variations.
Data Source
AI summary
A method of age and gender estimation, comprising receiving an input image, detecting a facial image within the input image, estimating a head pose based on a set of facial image intensities of the facial image, wherein the head pose is expressed as a yaw, a pitch and a roll, determining whether the yaw, the pitch and the roll of the head pose is less than a predetermined threshold, aligning the facial image if the yaw, the pitch and the roll of the head pose are less than the predetermined threshold and predicting an age and a gender of the aligned facial image.