Discriminative multi-manifold analysis for face recognition from a single training sample per person

By Jiwen Lu, Yap-Peng Tan, G. Wang. Vision, 2011

350 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2c48af4cd2100d02f0f9311a36acd528bf644f68

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. Jiwen Lu
2. Jie Zhou
3. G. Wang
4. Tian-Tsong Ng
5. Xiuzhuang Zhou
6. Venice Erin Liong
7. Jun Liu
8. Yap-Peng Tan

In Signal Processing

1. Jie Zhou
2. Jiwen Lu
3. Xiuzhuang Zhou
4. Venice Erin Liong
5. G. Wang
6. Tian-Tsong Ng
7. Yap-Peng Tan
8. Junlin Hu

Citations in the corpus

• Neighborhood repulsed metric learning for kinship verification (2012, neutral): “Representative examples include face recognition [5], [8], [10], [20], [22], [27], [28], [35], [37], [40], [41], [42], [43], [54], [55], [56], [58], [64], [65], [66], [67], [68], facial expression recognition [11], [18], [70], facial age estimation [19], [21], [24], [25], [31], [39], gender classifi”
• Learning deep features for multiple object tracking by using a multi-task learning strategy (2014, background): “It has a wide range of applications, such as human-machine interaction [1] and surveillance video analytics [2].”
• Learning Compact Binary Face Descriptor for Face Recognition (2015, background): “However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand.”
• Discriminative Deep Metric Learning for Face Verification in the Wild (2014, background): “Overt the past two decades, a large number of face recognition methods have been proposed in the literature [23, 41], and most of them have achieved satisfying recognition performance under controlled conditions.”
• PCANet: A Simple Deep Learning Baseline for Image Classification? (2014, neutral): “Dup-I taken within the period of three to JOURNAL OF LATEX CLASS FILES 9 TABLE 5 Recognition rates (%) on FERET dataset. Probe sets Fb Fc Dup-I Dup-II Avg. LBP [18] 93.00 51.00 61.00 50.00 63.75 DMMA [25] 98.10 98.50 81.60 83.20 89.60 P-LBP [21] 98.00 98.00 90.00 85.00 92.75 POEM [26] 99.60 99.50 88”