Face identification is an essential topic in surveillance system research. Surveillance systems have many unconstrained conditions, e.g., brightness, occlusion, and user state variations. In this paper, we propose a multi-SVM based face recognition method using a near-infrared camera. Our method has a face identification scenario optimized for an in-vehicle surveillance system, which comprises two steps: (i) registering a driver and (ii) recognizing whether the driver is a registered. We perform feature extraction and recognition for each facial landmark. In the case of extreme exposure to light, we convert normal face images into simulated light overexposed images for learning. Thus, face classifiers for normal and extreme illumination conditions are simultaneously generated. We also create a new face dataset and evaluate our method with both our new and PolyU NIR datasets. Experimental results show that we achieve significantly higher recognition accuracy than existing methods.
|Title of host publication||Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Feb 11|
|Event||15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018 - Auckland, New Zealand|
Duration: 2018 Nov 27 → 2018 Nov 30
|Name||Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance|
|Conference||15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018|
|Period||18/11/27 → 18/11/30|
Bibliographical noteFunding Information:
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government(MSIP) (No. 2016-0-00152, Development of Smart Car Vision Techniques based on Deep Learning for Pedestrian Safety) and the National Research Foundation of Korea grant funded by the Korean government (No. NRF-2017R1D1A1B 04035633).
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Media Technology