Facial Image Clustering for Video Indexing
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Solution Overview
Problem
Object detection and recognition in video content, particularly facial image identification, are challenging tasks in artificial intelligence due to high computational costs and low confidence levels in determining identical facial images across video clips.
Innovation Solution
A system and method for efficient object detection in video clips, utilizing a facial image extraction module to normalize and cluster facial images, comparing them to reference images, and storing metadata for efficient video indexing and search functionality, allowing for manual identification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used for facial image identification in video content, then detection accuracy may be maintained, but computational cost becomes prohibitively high
Solution Approach 1:
The patent segments the video processing task by first extracting only facial regions from video frames using face detection algorithms, then performing detailed recognition only on these segmented facial regions rather than processing entire video frames. This segmentation reduces computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent extracts key facial features and characteristics from detected facial images to create compact feature representations. By extracting only the essential identifying features rather than processing complete high-resolution facial images, the system reduces computational cost while preserving identification capability.
2Reliability
If comprehensive object detection is performed on all video content, then recognition completeness is improved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary face detection and localization before detailed recognition. By first identifying potential facial regions and preprocessing them (normalization, feature extraction), the system prepares data in advance for faster and more accurate recognition, reducing overall processing time while maintaining completeness.
Solution Approach 2:
The patent implements periodic processing by analyzing video frames at strategically selected intervals rather than continuously processing every frame. This periodic approach maintains recognition completeness by sampling key moments while significantly reducing processing time through selective frame analysis.
Data Source
AI summary
A method for processing digital media is described. The method, in one example embodiment, includes identification of objects in a video stream by detecting, for each video frame, an object in the video frame and selectively associating the object with an object cluster. The method may further include comparing the object in the object cluster to a reference object and selectively associating object data of the reference object with all objects within the object cluster based on the comparing. The method may further include manually associating the object data of the reference object with all objects within the object duster having no associated reference object and populating a reference database with the reference object for the object cluster.