Local descriptor margin projections (LDMP) for face recognition
- 157 Downloads
Feature extraction is a key problem in face recognition systems. This paper tackles this problem by combining the strength of image descriptor with dimensionality reduction technology. So, this paper proposes a new efficient face recognition method-local descriptor margin projections (LDMP). Firstly, we propose a novel local descriptor for face image representation. At this step, an effective and simple metric approach named gray value accumulating distance (GAD) is firstly proposed. And then a novel local descriptor based on GAD is presented to capture the local structure information between central pixel and its neighbors effectively. Secondly, we propose a dimensionality reduction algorithm named maximum margin learning projections (MMLP) which can obtain the low-dimensional and discriminative feature. Finally, experimental results on the Yale, Extended Yale B, PIE, AR and LFW face databases show the effectiveness of the proposed method.
KeywordsFace recognition Local descriptor Central pixel Feature extraction
This work is supported by the National Natural Science Fund of China (Grant Nos. 61503195, 61462064, 61203243,61402231, 61603192 and 61272077), the Natural Science Fund of Jiangsu Province (Grant No. BK20161580), the University Natural Science Fund of Jiangsu Province of China (Grant No. 15KJB520018, 16KJB520020 and 12KJA63001), the Project Funded by China Postdoctoral Science Foundation (Grant No. 2016M600433), the Project Funded by PAPD and CICAEET, and the Project supported by the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) (Grant No. 30916014107).
- 4.Yuan C, Sun X, Rui L (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications. 13(7):60–65Google Scholar
- 9.He XF, Yan SC, Hu YX et al (2003) Learning a locality preserving subspace for visual recognition. In: Proceedings of the 9th IEEE International Conference on Computer Vision, pp 385–392Google Scholar
- 20.Cai D, He XF, Zhou K et al (2007) Locality sensitive discriminant analysis. In: Proceedings of the IEEE International Conference on Artificial Intelligence, pp 708–713Google Scholar
- 23.Zhang WC, Shan SG, Gao W et al (2005) Local Gabor binary pattern histogram sequence (LGBPHS):a novel non-statistical model for face representation and recognition. In: Proceedings of the 10th IEEE International Conference on Computer Vision, pp 786–791Google Scholar
- 40.Liao SC, Zhu XX, Lei Z et al (2007) Learning multi-scale block local binary patterns for face recognition. In: Proceedings of the IEEE International Conference on Biometrics vol 4642, pp 828–837Google Scholar
- 44.Gross R, Matthews I, Cohn J et al (2007) The cmu multi-pose, illumination, and expression (multi-pie) face database. Technical report #07–08. Carnegie Mellon University Robotics InstituteGoogle Scholar
- 45.Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report #24Google Scholar
- 46.Huang GB, Ramesh M, Berg T et al (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report# 07–49. University of Massachusetts, AmherstGoogle Scholar
- 47.Wolf L, Hassner T, Taigman Y (2009) Similarity scores based on background samples. Computer Vision—ACCV 2009. Springer, Berlin, pp 88–97Google Scholar