Smart Robot Facial Expression Feedback Mechanism
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Solution Overview
Problem
Intelligent robots lack the ability to respond differently to user emotions, resulting in rigid and unengaging interactions, which reduces user experience.
Innovation Solution
A method for intelligent robots to capture and analyze facial expressions using image capture devices, detecting face information, predicting feature points, and employing recognition models to determine if a face is smiling, allowing for the output of preset emotional responses such as emoticons or voice responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the intelligent robot uses simple information interaction, then the device complexity is low, but the usage experience is poor due to rigid responses
Solution Approach 1:
The patent applies preliminary action by pre-training recognition models (face detection model, feature point prediction model, expression recognition model) before actual use. These models are prepared in advance to enable the robot to quickly perform emotional response without complex real-time processing, thus improving adaptability while controlling system complexity
Solution Approach 2:
The patent segments the facial expression recognition process into multiple independent modules: face detection module, feature point prediction module, and expression recognition module. Each module handles a specific task, making the overall system more manageable and less complex while achieving sophisticated emotional response capability
2Adaptability or versatility
If the intelligent robot captures and analyzes facial expressions, then the usage experience is enriched, but the loss of time increases due to multiple processing steps
Solution Approach 1:
The recognition models are pre-trained offline before deployment, so that during actual interaction, the robot can directly use these prepared models for rapid facial expression recognition without performing complex training computations in real-time, thus reducing processing time while maintaining interaction richness
Solution Approach 2:
The system dynamically adjusts the recognition process based on input quality and requirements, using optimized feature point prediction and expression recognition algorithms that can operate efficiently under different conditions, balancing processing time with interaction quality
Data Source
AI summary
An expression feedback method and a smart robot, belonging to smart devices. The method comprises: step S1, a smart robot using an image collection apparatus to collect image information; step S2, the smart robot detecting whether human face information representing a human face exists in the image information; if so, acquiring position information and size information associated with the human face information, and then turning to step S3; and if not, returning to step S1; step S3, according to the position information and the size information, obtaining, by prediction, a plurality of pieces of feature point information in the human face information and outputting same; and step S4, using a first identification model formed through pre-training, determining whether the human face information represents a smiling face according to the feature point information, and then exiting from this step; if so, the smart robot outputting preset expression feedback information; and if not, exiting from this step. The beneficial effect of the method is: enriching an information interaction content between a smart robot and a user, so as to improve usage experience of the user.