Face Attribute Recognition Model Precision via Variable Segmentation
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Solution Overview
Problem
Current face attribute recognition methods suffer from low precision in identifying face attributes such as age, gender, expression, and hair style due to limitations in feature extraction and classification processes.
Innovation Solution
A face attribute recognition method that utilizes an attribute recognition model trained on sample face images, employing different processing techniques for continuous and qualitative variables, and incorporating convolutional neural networks for feature extraction and SoftMax functions for improved accuracy, adjusts parameters using stochastic gradient descent to minimize loss values and achieve high recognition precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional face attribute recognition methods are used, then the recognition process can be completed, but the recognition precision is low
Solution Approach 1:
The patent segments the face attribute recognition process into distinct components: face detection to obtain coordinate frames, face registration for normalization, and attribute recognition model processing. This segmentation allows each component to be optimized independently, improving overall recognition precision by ensuring proper preprocessing before attribute extraction.
Solution Approach 2:
The patent employs parameter changes through the attribute recognition model that processes normalized face images through multiple layers of feature extraction. The model transforms input parameters (normalized face images) through sequential processing stages, adjusting parameters at each layer to progressively refine attribute predictions and achieve higher recognition precision.
2Measurement precision
If face detection and normalization processing are performed, then the input quality for attribute recognition is improved, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by performing face detection and normalization processing before attribute recognition. The face detection step obtains coordinate frames in advance, and face registration normalizes images beforehand, ensuring that the attribute recognition model receives pre-preprocessed input, which improves recognition precision while allowing parallel processing optimization.
Data Source
AI summary
A face attribute recognition method, electronic device, and storage medium. The method may include obtaining a face image, inputting the face image into an attribute recognition model, performing a forward calculation on the face image using the attribute recognition model to obtain a plurality of attribute values according to different types of attributes, and outputting the plurality of attribute values, the plurality of attribute values indicating recognition results of a plurality of attributes of the face image. The attribute recognition model may be obtained through training based on a plurality of sample face images, a plurality of sample attribute recognition results of the plurality of sample face images, and the different types of attributes.