Face Orientation Detection for Retail Customer Guidance
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Solution Overview
Problem
Current systems lack an effective method to accurately assess and respond to the state of mind of customers in retail environments, leading to inefficient customer service delivery.
Innovation Solution
An information processing apparatus that detects the orientation of a customer's face using cameras, estimates their state of mind through chronological changes in orientation and location, and outputs guidance information to direct customer service accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If face orientation detection and state of mind estimation are implemented, then customer service quality is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a detection unit for capturing face orientation data, an estimation unit for analyzing state of mind, and an output unit for generating guidance information. This segmentation allows each module to perform its specific function independently, improving overall system reliability while making the complexity manageable through modular design.
Solution Approach 2:
The estimation unit acts as an intermediary between the detection unit and the output unit. It processes the raw face orientation data from the detection unit and transforms it into meaningful state of mind assessments, which then guide the output unit's guidance information generation. This intermediary layer simplifies the overall system architecture by handling the complex analysis tasks in a dedicated component.
2Measurement precision
If chronological change analysis of face orientation is used to estimate state of mind, then accuracy of customer need identification is improved, but processing time increases
Solution Approach 1:
The detection unit captures face orientation data at periodic intervals, creating a chronological sequence of observations. The estimation unit analyzes these periodic measurements to detect patterns and changes in customer state of mind. This periodic sampling approach provides sufficient accuracy for identifying customer needs while avoiding continuous monitoring that would excessive processing time.
Solution Approach 2:
The system pre-establishes the framework for analyzing chronological changes in face orientation. By having the estimation unit ready to process sequential data and identify patterns, the system can quickly assess customer state of mind when needed, rather than requiring extensive real-time computation during critical service moments.
Data Source
AI summary
An information processing apparatus includes a detection unit that detects an orientation of the face of a customer in a store, an estimating unit that estimates the customer's state of mind in accordance with a chronological change of the detected orientation of the face of the customer, and an output unit that outputs guidance information that guides the customer to a customer service, in accordance with the estimated customer's state of mind.