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Y.: Descriptor based methods in the wild
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| Venue: | In: Faces in Real-Life Images Workshop in ECCV. (2008) (b) Similarity Scores based on Background Samples |
| Citations: | 70 - 13 self |
BibTeX
@INPROCEEDINGS{Wolf_y.:descriptor,
author = {Lior Wolf and Tal Hassner and Yaniv Taigman},
title = {Y.: Descriptor based methods in the wild},
booktitle = {In: Faces in Real-Life Images Workshop in ECCV. (2008) (b) Similarity Scores based on Background Samples},
year = {}
}
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Abstract
Abstract. Recent methods for learning similarity between images have presented impressive results in the problem of pair matching (same/notsame classification) of face images. In this paper we explore how well this performance carries over to the related task of multi-option face identification, specifically on the Labeled Faces in the Wild (LFW) image set. In addition, we seek to compare the performance of similarity learning methods to descriptor based methods. We present the following results: (1) Descriptor-Based approaches that efficiently encode the appearance of each face image as a vector outperform the leading similarity based method in the task of multi-option face identification. (2) Straightforward use of Euclidean distance on the descriptor vectors performs somewhat worse than the similarity learning methods on the task of pair matching. (3) Adding a learning stage, the performance of descriptor based methods matches and exceeds that of similarity methods on the pair matching task. (4) A novel patch based descriptor we propose is able to improve the performance of the successful Local Binary Pattern (LBP) descriptor in both multi-option identification and same/not-same classification. 1
Keyphrases
face image multi-option face identification method match similarity method euclidean distance pair matching successful local binary pattern labeled face multi-option identification straightforward use related task descriptor-based approach novel patch recent method learning stage not-same classification following result impressive result descriptor vector
