Facial Expression Analyzer for Video Contact Centers
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Solution Overview
Problem
In video contact centers, agents often rely on personal judgment during video calls, which may not lead to the best outcomes, as they cannot consistently assess and respond to callers' moods effectively.
Innovation Solution
A system that analyzes the caller's facial expressions to determine their mood in real-time, allowing for immediate actions such as alerting a supervisor or guiding the agent on how to react, and recording mood data for historical analysis and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If agents use personal judgment to assess caller mood, then the interaction can be flexible and adaptive, but the assessment becomes subjective and inconsistent
Solution Approach 1:
The patent replaces the human agent's subjective visual assessment mechanism with an automated facial expression analysis system. The system captures facial expressions via video, extracts geometric features (eye distance, mouth shape, eyebrow position), and classifies emotions using machine learning algorithms, eliminating human subjectivity while maintaining interaction flexibility.
Solution Approach 2:
The system enables self-service mood assessment by automatically analyzing the caller's facial expressions without requiring agent intervention for subjective judgment. The automated system independently performs mood detection and provides consistent results, allowing agents to focus on resolving customer issues rather than assessing emotions.
2Ease of operation
If agents manually monitor and respond to caller mood, then they can adjust conversation in real-time, but this increases the cognitive load and reduces productivity
Solution Approach 1:
The system continuously monitors facial expressions during the conversation and provides real-time feedback to agents through the interface. The feedback includes mood classification (happy, angry, neutral, etc.) and can trigger automated responses or alerts, enabling agents to adjust their conversation strategy without manual monitoring effort.
Solution Approach 2:
The automated system performs real-time mood detection and response generation without requiring agent cognitive resources. The system independently analyzes facial expressions, determines appropriate responses, and can even initiate conversations or transfer calls automatically, freeing agents from manual real-time decision-making.
3Measurement precision
If the system analyzes facial expressions continuously, then mood detection accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system segments the facial recognition task into distinct modules: facial landmark detection (identifying key points like eyes, nose, mouth), geometric feature extraction (calculating distances and angles between landmarks), and emotion classification (mapping features to emotional states). This modular approach improves accuracy while making the system more manageable and easier to implement.
Solution Approach 2:
Instead of analyzing every pixel of the video feed, the system focuses on specific partial regions (facial landmarks) that are most indicative of emotion. By concentrating computational resources on these critical features rather than the entire face, the system achieves high accuracy with reduced complexity.
Data Source
AI summary
In one embodiment, a method determines an indication of a mood for a caller during a service call. The mood may be determined using a facial analysis of the caller's facial expressions. The mood may indicate an emotion of the user, such as the user is angry, happy, etc. The mood may be determined based on a facial expression analysis of the caller during a portion of the service call. The service call may be a call between the caller and a service center, which may provide customer support to a caller for a product, service, etc. One example of a service center may be video contact service center that enables video calls with a caller. An action is then determined based on analysis of the mood invoked during a portion of the call. Once the action is determined, the action may be performed.