Matching Forensic Sketches to Mug Shot Photos

By Brendan Klare, Zhifeng Li, Anil K. Jain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011

414 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/31a1015454953d637f86a89a11e98c8c3cf43b40

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. Tao A. Xiang
2. Xiaogang Wang
3. Luc Van Gool
4. Dacheng Tao
5. Yi Yang
6. Timothy M. Hospedales
7. Yu Qiao
8. Yi-Zhe Song

In Signal Processing

1. S. Li
2. Zhen Lei
3. Ran He
4. Zhenan Sun
5. Xiang Wu
6. Xiaogang Wang
7. Tien-Ping Tan
8. Xiaoming Liu

Citations in the corpus

• Learning Discriminant Face Descriptor (2014, background): “Besides LBP and its variants (TPLBP, FPLBP [33]), LPQ [52], SIFT [53], and HOG [54], 2 which are popular descriptors in heterogeneous face recognition [55], [56], are also compared.”
• Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval (2017, background): “With the proliferation of touch-screen devices, a number of sketch-based computer vision problems have attracted increasing attention, including sketch recognition [47, 36, 3, 32], sketch-based image retrieval [46, 24, 10], sketch-based 3D model retrieval [39], and forensic sketch analysis [14, 28].”
• Coupled information-theoretic encoding for face photo-sketch recognition (2011, methodology): “We test our method with the same configuration of training and testing splits as [31, 14].”
• The CASIA NIR-VIS 2.0 Face Database (2013, methodology): “txt are for testing, where [1-10] denotes the id of sub-experiment.”
• Sketch-a-Net: A Deep Neural Network that Beats Humans (2016, background): “…(Eitz et al, 2012; Schneider and Tuytelaars, 2014; Yu et al, 2015), sketch-based image retrieval (Eitz et al, 2011; Hu and Collomosse, 2013), sketchbased shape (Zou et al, 2015) or 3D model retrieval (Wang et al, 2015), and forensic sketch analysis (Klare et al, 2011; Ouyang et al, 2014).”
• Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition (2017, methodology): “In addition, [37] applied a sparse representation to learn modality-invariant features, and [38] further applied densely sampled SIFT and multi-block LBP features to represent heterogeneous face images.”