Illumination Invariant Face Recognition Using Near-Infrared Images

By S. Li, Rufeng Chu, Shengcai Liao, Lun Zhang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

656 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/02fda07735bdf84554c193811ba4267c24fe2e4a

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. Xiaogang Wang
2. S. Li
3. Dacheng Tao
4. Zhen Lei
5. Yi Yang
6. Luc Van Gool
7. Andrew Zisserman
8. Bernt Schiele

In Signal Processing

1. Zhen Lei
2. S. Li
3. Matti Pietikäinen
4. Guoying Zhao
5. Sébastien Marcel
6. Shengcai Liao
7. Xiaobai Li
8. Dong Yi

Citations in the corpus

• 50 years of biometric research: Accomplishments, challenges, and opportunities (2016, neutral): “Consequently , most of the research in biometric recognition has rightly focused on the following two fundamental problems:”
• The CASIA NIR-VIS 2.0 Face Database (2013, methodology): “For example, the NIR based face recognition method [8] has been developed to overcome the illumination variation problem; sketch images drawn by artists based on the recollection of an eyewitness have been used in the retrieval of a sketch from the police mugshot databases.”
• Getting to Know Low-light Images with The Exclusively Dark Dataset (2018, methodology): “Common object detection is not usually addressed in surveillance works, although the closest would be face recognition ([20], [21]) and pedestrian detection ([22], [23], [24], [25], [26]).”
• Face liveness detection by learning multispectral reflectance distributions (2011, methodology): “As current near-infrared face recognition systems also adopt 850nm as light source [2], we choose 1450nm and 850nm as our consideration.”
• A Spontaneous Micro-expression Database: Inducement, collection and baseline (2013, background): “For the recording of the latter ten participants (recorded 5 months later than the first ten participants), in addition to the high speed camera another integrated camera box was added (details about the camera provided in [26]), which consists of a normal visual camera (VIS) and a near-infrared (NI”
• Heterogeneous Face Recognition Using Kernel Prototype Similarities (2013, neutral): “In this paper, 1 ¼ 2 and 2 ¼ 4.”
• Personal Authentication Using Hand Vein Triangulation and Knuckle Shape (2009, background): “The face identification beyond visible spectrum is increasing receiving attention and has highest user acceptance [7], [23].”
• Learning Face Representation from Scratch (2014, methodology): “Then, various local feature based methods emerged and they were naturally used by combining with the above linear models, such as Gabor+LDA [18], LBP+LDA [15] and so on.”
• Recognizing Rotated Faces From Frontal and Side Views: An Approach Toward Effective Use of Mugshot Databases (2008, methodology): “In this paper, we report the complete and improved formulation, thorough investigation and extended experimental evaluation of our system.”