Incremental Face Clustering via Hybrid Classifier
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Solution Overview
Problem
Existing face recognition systems face inefficiencies due to high computational costs and latency caused by traditional clustering techniques, which require cubic time and memory, and necessitate full re-clustering upon new data additions, making them impractical for large-scale deployments.
Innovation Solution
A hybrid approach combining clustering algorithms with classifier models for incremental clustering and classification, allowing for linear-time assignment of cluster labels and reducing the need for frequent re-clustering, using face embeddings and classification models like Extreme Value Machines to process new data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering techniques are used for face recognition, then clustering accuracy is maintained, but computational time and memory usage increase significantly
Solution Approach 1:
The patent segments the clustering process into two distinct phases: (1) full re-clustering performed periodically on the complete dataset to maintain accuracy, and (2) incremental clustering for new data points that leverages existing cluster structures. This segmentation allows the system to achieve accurate clustering results while dramatically reducing computational time for routine operations.
Solution Approach 2:
The system performs preliminary full clustering to establish baseline cluster structures and centroids before new data arrives. These pre-computed cluster representations are then reused during incremental clustering operations, avoiding the need to recompute entire cluster structures from scratch and thus reducing computational time while maintaining accuracy.
2Reliability
If traditional clustering techniques are used for face recognition, then comprehensive clustering is achieved, but memory consumption increases to O(n2) or more
Solution Approach 1:
The patent extracts and stores only the essential cluster representations (centroids and assignment structures) from the full clustering process, rather than maintaining all pairwise distance matrices or complete cluster member lists. This extraction reduces memory consumption from O(n²) to O(n) while preserving the ability to perform comprehensive clustering when needed.
Solution Approach 2:
The system changes the parameter representation of cluster data from storing complete pairwise relationships to storing condensed cluster centroids and assignment vectors. This parameter transformation maintains clustering completeness and reliability while dramatically reducing the memory footprint from quadratic to linear complexity.
3Measurement precision
If full re-clustering is performed whenever new data is added, then clustering accuracy is maintained, but processing speed decreases
Solution Approach 1:
The patent implements a dynamic clustering strategy that adapts its approach based on data arrival patterns. For incremental data additions, the system uses fast incremental clustering that leverages existing structures. For periodic updates or when accuracy thresholds are not met, the system transitions to full re-clustering. This dynamic adaptation maintains accuracy while maximizing processing speed for routine operations.
Solution Approach 2:
The system performs full re-clustering periodically rather than continuously upon every data addition. Between periodic full clustering operations, incremental clustering handles new data efficiently. This periodic action schedule ensures clustering accuracy is maintained through regular full re-clusterings while achieving high processing speeds during incremental updates.
4Adaptability or versatility
If existing clustering systems are deployed to production environments with large traffic, then face recognition capability is provided, but computational expense and wait time become problematic
Solution Approach 1:
The patent enables continuous face recognition operations in production environments by maintaining cluster structures continuously available through incremental updates. The system processes new face data continuously without interrupting service, using incremental clustering to update cluster assignments in real-time. This continuity eliminates the need to stop service for re-clustering, reducing both computational expense and wait time while maintaining deployment capability.
Data Source
AI summary
Techniques for improved image classification are provided. Face embeddings are generated for each face depicted in a collection of images, and the face embeddings are clustered based on the individual whose face is depicted. Based on these clusters, each embedding is assigned a label reflecting the cluster assignments. Some or all of the face embeddings are then used to train a classifier model to generate cluster labels for new input images. This classifier model can then be used to process new images in an efficient manner, and classify them into appropriate clusters.