Multi-Task Neural Network Recognizer for Facial Attributes
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Solution Overview
Problem
Existing face recognition technologies are inefficient in simultaneously recognizing multiple elements such as identity, gender, age, ethnic group, attractiveness, facial expression, and emotion from a single input image, requiring excessive computational resources and being limited in recognition accuracy.
Innovation Solution
A method and apparatus utilizing a neural network-based recognizer that performs multi-task recognition by generating feature images and filtering them through convolutional layers to extract higher-complexity features, allowing for simultaneous recognition of multiple elements with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate recognizers are used to recognize different facial elements (identity, gender, age, etc.), then recognition accuracy for each element can be maintained, but computational resources and system complexity increase significantly
Solution Approach 1:
The patent combines multiple separate recognition tasks (identity, gender, age, ethnic group, attractiveness, facial expression, emotion) into a single unified neural network recognizer. This single recognizer processes the input image once and simultaneously outputs all required recognition results, eliminating the need for multiple separate recognizers and reducing system complexity while maintaining recognition accuracy through shared feature extraction layers.
Solution Approach 2:
The neural network recognizer is designed with multi-functionality to perform diverse recognition tasks simultaneously. The same recognizer structure handles different types of facial attributes (demographic information, emotional states, physical characteristics) by processing them through shared convolutional layers and specialized output layers, making the system universal rather than task-specific.
2Adaptability or versatility
If multiple separate recognizers are used to recognize different facial elements, then comprehensive recognition coverage is achieved, but computational resources and processing time increase
Solution Approach 1:
The patent merges multiple recognition functions into a single neural network that processes the input image once. The shared convolutional base extracts features common to all recognition tasks, and specialized head layers generate outputs for different attributes simultaneously, reducing redundant computation and energy consumption compared to running multiple separate recognizers.
Solution Approach 2:
The neural network performs preliminary feature extraction in shared convolutional layers that capture general facial characteristics useful for all subsequent recognition tasks. This preliminary processing avoids redundant feature extraction for each individual attribute, optimizing computational resource usage while maintaining comprehensive recognition coverage.
3Ease of manufacture
If traditional face recognition methods are used, then implementation simplicity is maintained, but recognition accuracy for multiple elements simultaneously is limited
Solution Approach 1:
The patent replaces traditional mechanical or rule-based face recognition methods with a neural network-based system. The neural network automatically learns optimal feature representations and recognition patterns through training data, substituting manual feature engineering and complex rule-based systems with an adaptive learning model that achieves higher accuracy while maintaining a relatively simple unified implementation structure.
Data Source
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AI summary
A recognition method includes receiving an input image; and recognizing a plurality of elements associated with the input image using a single recognizer pre-trained to recognize a plurality of elements simultaneously.