Face sketch recognition

By Xiaoou Tang, Xiaogang Wang. IEEE transactions on circuits and systems for video technology (Print), 2004

375 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/79a176d432659450524290555460947ef7cdd895

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

A nonlinear approach for face sketch synthesis and recognition (2005, methodology): “An alternative approach is to generate a pseudo-sketch from a photo image [9,10,11], which is used in our scheme.”
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014, background): “By contrast, there have already been a number of papers comparing human and computer-based face veriﬁcation performance [2, 54, 40, 41, 38, 8].”
The CASIA NIR-VIS 2.0 Face Database (2013, background): “Representative works in this category include [14], [11] and [16], where the most representative one is eigen-transform.”
Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch (2011, methodology): “For example a holistic mapping [ 1 ] is used to convert a photo image into a corresponding sketch image.”
Face Photo-Sketch Synthesis and Recognition (2009, methodology): “It can not only help police locate a group of potential suspects, but also help the witness and the artist modify the sketch drawing of the suspect interactively based on similar photos retrieved [1], [2], [3], [4], [5], [6], [7].”
Coupled information-theoretic encoding for face photo-sketch recognition (2011, background): “This application leads to a number of studies on this topic [26, 27, 28, 31, 9, 14, 6].”
A Comprehensive Survey to Face Hallucination (2013, methodology): “This method assumes that the sketch and corresponding photo share the same linear combination coefficients (Tang and Wang 2002, 2004).”
Deep Learning Identity-Preserving Face Space (2013, methodology): “In the future work, we will extend the framework to deal with robust face recognition in other difficult conditions such as expression change and face sketch recognition [25, 30], and will combine FIP features with more classic face recognition approaches to further improve the performance [28, 29, ”
Heterogeneous Face Recognition Using Kernel Prototype Similarities (2013, background): “The motivation behind heterogeneous face recognition is that circumstances exist in which only a particular modality of a face image is available for querying a large database of mug shots (visible band face images).”