Abstract
This paper proposes an ensemble of different state-of-art algorithms for realizing a face search system aimed at achieving higher accuracies compared to any single algorithm. This is achieved by leveraging most promising deep networks (Facenet, OpenFace, DeepFace, and VGGFace – originally trained for face recognition) and different Approximate Nearest Neighbor Search (ANNS) algorithms (Annoy and LSHash). Face images in the database are subjected to feature extraction (embeddings computed by deep networks) and indexing (in set structure for faster search) by ANNS algorithms. An input face query image is processed in the following four stages. First, the face region is detected from the query image and appropriately aligned for further processing. Second, the facial features are extracted using multiple deep networks. Third, the ANNS algorithms perform fast search by efficiently shrinking the gallery size from millions to a few hundred faces. Fourth, a fine matching is performed using two different methods separately to produce the final search results. These are (a) cosine similarity and (b) score-based matching and re-ranking of results. The experimental results demonstrate the diversity in results obtained by use of multiple feature extractors and ANNS techniques and the accuracy achieved by using the proposed ensemble framework.
S. Vats and S. Jain—These authors have an equal contribution.
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Vats, S., Jain, S., Guha, P. (2021). A Novel Ensemble Framework for Face Search. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_43
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