Automatic Image Correlation Using Facial Recognition and Location Data
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Solution Overview
Problem
Current digital image sharing processes are manual and inefficient, as they require photographers to manually identify and select contact methods for individuals in images, which can be complicated due to similarities in facial features and multiple contact methods, leading to inaccuracies and increased complexity.
Innovation Solution
A method that uses facial recognition software in conjunction with location data to automatically identify individuals in images and determine the preferred method of transmission, utilizing a computer system that processes image data, contact information, and location data to streamline the sharing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual identification and selection of contact methods is used, then the photographer has control over the sharing process, but the process becomes time-consuming and complex
Solution Approach 1:
The system performs automatic identification of individuals in images and selection of their preferred contact methods without requiring manual user intervention. The computer system independently analyzes facial features, compares them with contact database images, retrieves associated contact information, and determines transmission methods automatically, allowing the system to serve itself rather than requiring continuous user input.
Solution Approach 2:
The system pre-establishes a database of contact information including multiple contact methods for each individual, and pre-processes facial recognition data during image capture. When an image needs to be shared, the identification and contact selection have already been prepared or can be quickly retrieved, eliminating the need for time-consuming manual processes at the moment of sharing.
2Extent of automation
If facial recognition software is used alone, then identification can be automated, but accuracy decreases when individuals have similar facial features
Solution Approach 1:
The system combines multiple identification approaches: facial recognition analysis of physical features, analysis of location data from GPS coordinates, and cross-referencing with contact database information. By merging these different data sources and analysis methods, the system achieves higher identification accuracy than facial recognition alone, particularly when individuals have similar facial features.
Solution Approach 2:
Location data serves as an intermediary element that bridges the gap when facial recognition alone is insufficient. The system uses geographic location information from the image metadata to narrow down which contacts were present at that location, then applies facial recognition within this filtered set, significantly improving accuracy for individuals with similar features.
3Adaptability or versatility
If multiple contact methods are stored for each contact, then the system has flexibility in transmission options, but the complexity of selecting the preferred method increases
Solution Approach 1:
The system automatically determines the preferred contact method by analyzing the retrieved contact information and applying selection criteria without requiring user intervention. The computer system independently evaluates available contact methods (email, text, social media, etc.) and selects the most appropriate one based on the contact's preferences and the context, eliminating the need for users to manually choose from multiple options.
Solution Approach 2:
The system extracts and prioritizes the preferred contact method from the multiple stored contact methods for each individual. By separating the preferred method selection from the overall contact selection process and pre-organizing contact information with designated preferences, the system simplifies the transmission selection while maintaining flexibility in offering multiple options.
Data Source
AI summary
Embodiments of the present invention provide systems and methods for image correlation and distribution. The method includes receiving an image depicting at least one person, metadata for the image, contact data, facial recognition data, and location data. The method further includes analyzing the image and other data, and determining the identity of people in the image based on the facial recognition data and the location data.