TV Face Recognition for Login-Free Video Recommendation
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Solution Overview
Problem
Existing television systems require users to log in with their accounts for video recommendations, which is a cumbersome process and not user-friendly.
Innovation Solution
A face recognition-based method that acquires a user's face image, extracts face features, and compares them with an archive to recommend personalized or public video content, reducing the need for manual login.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users log in with their accounts to get video recommendations, then the recommendation accuracy is improved, but the operation complexity increases
Solution Approach 1:
The system performs automatic face recognition and account identification without requiring manual user input. The television system autonomously captures facial images, extracts features, matches them with archived face data, and identifies users automatically, eliminating the need for users to manually log in while maintaining accurate recommendations.
Solution Approach 2:
The patent replaces the mechanical/manual login process with an optical recognition system. Instead of requiring users to physically enter credentials, the system uses a camera to capture facial images and processes them through feature extraction and matching algorithms to automatically identify users and retrieve their account information.
2Ease of operation
If face recognition technology is introduced to enable automatic identification, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The face recognition system is divided into distinct functional modules: facial image capture, face feature extraction, face feature matching, and user identification. Each module performs a specific function independently, making the overall complex system manageable and easier to implement through modular components.
Solution Approach 2:
The system pre-extracts and stores face features of registered users in an archive before actual use. When a user approaches the television, the system quickly compares the captured face image against the pre-stored features, enabling rapid identification without requiring complex real-time processing during the actual login moment.
Data Source
AI summary
Disclosed are a face recognition-based video recommendation method and an apparatus, and a television. The method includes: acquiring a face image, the face image comprising a face of at least one user (S1); acquiring, according to a face recognition algorithm, a face feature set of the at least one user from the face image (S2); comparing the face feature set with face feature sets in an archive (S3); and if the face feature set of the at least one user matches the face feature sets in the archive, displaying a first video recommendation set, and if each face feature set does not match the face feature sets in the archive, display a second video recommendation set (S4). Using the method to recommend videos to users reduces operation steps of the users choosing to watch videos and provides convenience for the users to use.