Face Image Retrieval Revisited
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-25087-8_19 |
| Field | Informatics |
| Keywords | face retrieval; face detection; effectiveness; efficiency; face matcher fusion |
| Description | The objective of face retrieval is to efficiently search an image database with detected faces and identify such faces that belong to the same person as a query face. Unlike most related papers, we concentrate on both retrieval effectiveness and efficiency. High retrieval effectiveness is achieved by proposing a new fusion approach which integrates existing state-of-the-art detection as well as matching methods. We further significantly improve a retrieval quality by employing the concept of multi-face queries along with optional relevance feedback. To be able to efficiently process queries on databases with millions of faces, we apply a specialized indexing algorithm. The proposed solutions are compared against four existing open-source and commercial technologies and experimentally evaluated on the standardized FERET dataset and on a real-life dataset of more than one million face images. The retrieval results demonstrate a significant gain in effectiveness and two-orders of magnitude more efficient query processing, with respect to a single technology executed sequentially. |
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