Depth-based face recognition using local quantized patterns adapted for range data

By Tomás Mantecón, Carlos R. del-Blanco, F. Jaureguizar, Narciso García. 2014

26 citations recorded by the source corpus.

A depth-based face recognition algorithm specially adapted to high range resolution data acquired by the new Microsoft Kinect 2 sensor is presented. A novel descriptor called Depth Local Quantized Pattern descriptor has been designed to make use of the extended range resolution of the new sensor. This descriptor is a substantial modification of the popular Local Binary Pattern algorithm. One of the main contributions is the introduction of a quantification step, increasing its capacity to distinguish different depth patterns. The proposed descriptor has been used to train and test a Support Vector Machine classifier, which has proven to be able to accurately recognize different people faces from a wide range of poses. In addition, a new depth-based face database acquired by the new Kinect 2 sensor have been created and made public to evaluate the proposed face recognition system.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Li Liu
2. Jungong Han
3. Ling Shao
4. Ziyun Cai
5. Yi-Zhe Song
6. Jianbing Shen
7. Wenguan Wang
8. Tao A. Xiang

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “7 High resolution range based face dataset (HRRFaceD) The image processing group in Polytechnic University of Madrid created high resolution range based face dataset (HRRFaceD) [62] in 2014, intending to evaluate the recognition of different faces from a wide range of poses.”