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Clustering Millions of Faces by Identity (1604.00989v1)

Published 4 Apr 2016 in cs.CV

Abstract: In this work, we attempt to address the following problem: Given a large number of unlabeled face images, cluster them into the individual identities present in this data. We consider this a relevant problem in different application scenarios ranging from social media to law enforcement. In large-scale scenarios the number of faces in the collection can be of the order of hundreds of million, while the number of clusters can range from a few thousand to millions--leading to difficulties in terms of both run-time complexity and evaluating clustering and per-cluster quality. An efficient and effective Rank-Order clustering algorithm is developed to achieve the desired scalability, and better clustering accuracy than other well-known algorithms such as k-means and spectral clustering. We cluster up to 123 million face images into over 10 million clusters, and analyze the results in terms of both external cluster quality measures (known face labels) and internal cluster quality measures (unknown face labels) and run-time. Our algorithm achieves an F-measure of 0.87 on a benchmark unconstrained face dataset (LFW, consisting of 13K faces), and 0.27 on the largest dataset considered (13K images in LFW, plus 123M distractor images). Additionally, we present preliminary work on video frame clustering (achieving 0.71 F-measure when clustering all frames in the benchmark YouTube Faces dataset). A per-cluster quality measure is developed which can be used to rank individual clusters and to automatically identify a subset of good quality clusters for manual exploration.

Citations (169)

Summary

  • The paper introduces an innovative rank-order clustering method that significantly enhances the scalability of processing millions of unlabeled face images.
  • It employs randomized k-d trees for efficient nearest neighbor computation, achieving an F-measure of up to 0.87 on benchmark datasets.
  • The method demonstrates practical potential for social media analytics and forensic investigations, paving the way for future enhancements in face recognition.

Efficient Identity Clustering for Large Collections of Unlabeled Face Images

The paper "Clustering Millions of Faces by Identity" introduces an innovative method designed to address the complex problem of clustering vast datasets of unlabeled face images into individual identities. This approach is pertinent to applications in social media and law enforcement, where datasets can involve hundreds of millions of faces, making traditional clustering methods impractical. The authors propose and refine a Rank-Order clustering algorithm, enabling both enhanced scalability and superior clustering accuracy compared to other methods such as k-means and spectral clustering.

Key Contributions and Methodology

  1. Rank-Order Clustering Adaptations: The authors implement a Rank-Order clustering technique initially proposed by Zhu et al., but incorporate modifications to tackle the challenges posed by large datasets. They introduce an approximate method leveraging randomized k-d trees for scalable nearest neighbor computations, essential when dealing with datasets on the order of 123 million images.
  2. Performance Metrics and Dataset Utilization: Evaluation is conducted across multiple datasets, including the Labeled Faces in the Wild (LFW) dataset with 13K images and a distractor set with up to 123M images. The proposed algorithm achieves an F-measure of 0.87 on the LFW dataset and 0.27 when extended with the distractor data. This demonstrates a trade-off between scalability and precision, common in large-scale identity clustering tasks.
  3. Preliminary Work on Video Frame Clustering: Beyond static images, the paper explores video frame clustering using the YouTube Faces dataset. Achieving an F-measure of 0.71 indicates the potential applicability of their method to dynamic face recognition tasks, though certain limitations concerning cross-video identity clustering are noted.
  4. Per-Cluster Quality Metrics: A per-cluster quality measure is developed, enabling the identification of high-quality clusters that could be prioritized for manual examination. This metric serves as an invaluable tool in forensic investigations and social media data analysis, where large volumes of clustered data require efficient triage.

Implications and Future Research Directions

The implications of this research are multifaceted, impacting both practical applications and theoretical advancements in face recognition and clustering algorithms. The scalable nature of the proposed method aligns well with the growing volume of visual data generated worldwide, allowing for practical usage in real-time scenarios such as social media analysis and forensic triage. The theoretical insights gained from adapting the rank-order algorithm could inspire further research into scalable clustering approaches for high-dimensional data.

Future developments may focus on integrating stronger face representations, further optimizing algorithmic performance, and enhancing cross-video identity clustering capabilities. As deep learning models continue to evolve, coupling advanced feature extraction techniques with scalable clustering methods will enhance precision, speed, and applicability across diverse datasets.

Conclusion

This paper makes significant strides in clustering vast arrays of face images by identity, addressing run-time complexities and overcoming traditional scalability barriers. It delivers promising results that establish a foundation for future research and practical implementations in an era where visual data proliferation demands efficient, accurate processing strategies. The research paves the way for enhanced identity verification systems, with wide applications stretching across technological, social, and legal domains.