Face recognition based on non-corresponding region matching

By Annan Li, Shiguang Shan, Xilin Chen, Wen Gao. 2011

22 citations recorded by the source corpus.

In previous works of face recognition, similarity between faces is measured by comparing corresponding face regions. That is to say, matching eyes with eyes and mouths with mouths etc‥ In this paper, we propose that face can be also recognized by matching non-corresponding facial regions. In another word face can be recognized by matching eyes with mouths, for example. Specifically, the problem we study in this paper can be formulated as how to measure the possibility whether two non-corresponding face regions belong to the same face. We propose that the possibility can be measured via canonical correlation analysis. Experimental results show that it is feasible to recognize face via non-corresponding region matching. The proposed method provides an alternative and more flexible way to recognize faces.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Liang Wang
2. Tien-Ping Tan
3. Ran He
4. Dacheng Tao
5. Shu Wu
6. Qiyue Yin
7. Shiming Xiang
8. Chunhong Pan

Citations in the corpus

• Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces (2017, neutral): “Methods such as those presented in [29, 13, 27, 22] use Canonical Correlation Analysis (CCA) for establishing relationships between data of different modalities.”
• A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “It has been widely used for cross-media retrieval[13], [84], [85], cross-lingual retrieval [86] and some vision problems [87].”
• Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation (2014, methodology): “• CCA The CCA method is the most widely used algorithm in the multimedia field (Hardoon, Szedm´ak, and Shawe-Taylor 2004; Hwang and Grauman 2010; Rasiwasia et al. 2010; Li et al. 2011).”
• Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval (2016, methodology): “[34] apply CCA to face recognition based on non-corre-”
• Learning Coupled Feature Spaces for Cross-Modal Matching (2013, methodology): “Specifically, CCA is probably the most popular one due to its wide-spread use in cross-media retrieval[7, 19], crosslingual retrieval [25] and some vision problems [13, 14].”