Abstract
In this paper a supervised codebook learning technique for the Bag-of-Features representation that optimizes the learned codebooks towards face retrieval is proposed. This allows to use significantly smaller codebooks reducing both the storage requirements and the retrieval time allowing the proposed technique to efficiently scale to large datasets. The proposed method is also combined with a spatial image segmentation technique that exploits the natural symmetry of the human face to further reduce the size of the extracted representation. It is experimentally demonstrated using one large-scale face recognition dataset, the YouTube Faces Database, as well as two smaller datasets, that the proposed technique can increase the retrieval precision, while reducing the encoding time by almost two orders of magnitude.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: Ms-celeb-1m: challenge of recognizing one million celebrities in the real world. In: IS&T International Symposium on Electronic, Imaging (2016)
Iosifidis, A., Tefas, A., Pitas, I.: Discriminant bag of words based representation for human action recognition. Pattern Recogn. Lett. 49, 185–192 (2014)
Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. Int. J. Comput. Vis. 87(3), 316–336 (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Computing Research Repository, abs/1412.6980 (2014)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2169–2178 (2006)
Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)
Lian, X.-C., Li, Z., Lu, B.-L., Zhang, L.: Max-margin dictionary learning for multiclass image categorization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 157–170. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_12
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Passalis, N., Tefas, A.: Entropy optimized feature-based bag-of-words representation for information retrieval. IEEE Trans. Knowl. Data Eng. 28, 1664–1677 (2016)
Passalis, N., Tefas, A.: Spectral clustering using optimized bag-of-features. In: Proceedings of the 9th Hellenic Conference on Artificial Intelligence, p. 19 (2016)
Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)
Wang, C., Wang, Y., Zhang, Z.: Patch-based bag of features for face recognition in videos. Biometric Recogn. 7701, 1–8 (2012)
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 529–534 (2011)
Wolf, L., Hassner, T., Taigman., Y.: Descriptor based methods in the wild. In: Real-Life Images workshop at the European Conference on Computer Vision (ECCV) (2008)
Wu, Z., Ke, Q., Sun, J., Shum, H.-Y.: Scalable face image retrieval with identity-based quantization and multireference reranking. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1991–2001 (2011)
Yang, S., Bebis, G., Chu, Y., Zhao, L.: Effective face recognition using bag of features with additive kernels. J. Electron. Imaging 25(1), 013025 (2016)
Zhang, X., Gao, Y.: Face recognition across pose: a review. Pattern Recogn. 42(11), 2876–2896 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Passalis, N., Tefas, A. (2017). Spatial Bag of Features Learning for Large Scale Face Image Retrieval. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-47898-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47897-5
Online ISBN: 978-3-319-47898-2
eBook Packages: EngineeringEngineering (R0)