Video Face Recognition System for Automated Event Execution
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Solution Overview
Problem
Manually executing events for multiple individuals is time-intensive and prone to errors, especially when recording occasions that require group participation, necessitating a more efficient method to extract and utilize data from video frames to identify individuals and execute events.
Innovation Solution
A system that receives a video, splits it into frames, uses face detection and clustering algorithms, applies facial recognition to generate image hashes, and compares these hashes to stored hashes to identify individuals and transmit requests for contributions, allowing for accurate and efficient execution of events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to execute events for multiple individuals, then flexibility and adaptability are maintained, but time consumption increases and error rates increase
Solution Approach 1:
The system enables self-service by automatically extracting individual data from group video frames, identifying faces, and executing events without requiring manual data entry. The video processing system performs identification and event distribution autonomously, reducing human intervention to minimal confirmation steps.
Solution Approach 2:
The patent replaces the mechanical manual process of data entry and individual identification with an automated computer vision system. Face detection algorithms, clustering algorithms, and video processing mechanisms substitute for manual typing and searching, dramatically improving efficiency while reducing errors.
2Productivity
If automated video processing is implemented to identify individuals, then time efficiency improves and error reduction is achieved, but system complexity increases
Solution Approach 1:
The system segments the complex task of individual identification into distinct modular components: face detection algorithm, face clustering algorithm, and video frame processing. Each component handles a specific aspect of the identification process, making the overall system more manageable and maintainable despite its complexity.
Solution Approach 2:
The patent introduces intermediary processing layers including frame extraction from video, face region detection, and hash generation as intermediate steps between raw video input and final identification. These intermediaries simplify the core matching logic by preparing standardized data representations.
Data Source
AI summary
Embodiments of the present invention provide a system for executing multiple events in response to receiving a video and extracting identity and contact information from that video. A video showing multiple individuals associated with the multiple events is analyzed by splitting the video into individual frames and identifying unique faces in the video. A facial recognition process and an image hashing process are then applied to generate image hashes for each individual. These image hashes are then compared to known, stored image hashes to determine an identity and contact information for each individual. Once this information is collected, the system executes the multiple events based on the determined information about each individual.