Abstract
With the popularization of face recognition technology, the demand for the accuracy of face recognition has greatly increased. In the face image acquisition, the low illumination environment has a significant effect on the quality of human face images. Face images are susceptible to many factors such as image background, brightness, and image noise. This leads to many problems such as low detection rate of face recognition and the decline of recognition accuracy. In this paper, we take the face images under low illumination condition as a sample and present an adaptive algorithm based on the OTSU segmentation algorithm, which realizes the self-adaptation image acquisition under low illumination condition. By using the Adaboost classification detector to verify low-light face images before and after processing, the model we used in this paper successfully improved the accuracy of face detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Niu, S., Chen, Q., de Sisternes, L., Ji, Z., Zhou, Z., & Rubin, D. L. (2017). Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recognition, 61, 104–119.
Sarkar, S., Bhairannawar, S. S., KB, R., & Venugopal, K. R. (2015). FPGA implementation of moving object and face detection using adaptive threshold. International Journal of VLSI design & Communication Systems, 6(5), 15–35.
Sharifara, A., Rahim, M. S. M., & Anisi, Y. (2014). A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection. In 2014 International Symposium on Biometrics and Security Technologies (ISBAST) (pp. 73–78). IEEE.
Lv, Z., Wang, K., Zou, G., & Yuan, L. (2013). Illumination compensation method for face image based on improved gamma correction. In 2013 32nd Chinese Control Conference (CCC) (pp. 3733–3737). IEEE.
Kuo, S. C., Lin, C. J., & Peng, C. C. (2014). Using Adaboost method for face detection and pedestrian-flow evaluation of digital signage. In 2014 International Symposium on Computer, Consumer and Control (IS3C) (pp. 90–93). IEEE.
Cimpoi, M., Maji, S., & Vedaldi, A. (2015). Deep filter banks for texture recognition and segmentation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3828–3836). IEEE.
Acknowledgements
This work is funded by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2012BAH91F03) and Digital Imaging Theory-GK188800299016-054 and Hangzhou Dianzi University Graduate Innovative Research Fund-CXJJ2018017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, A., Wang, Q., Cao, J. (2019). Research on Adaptive Face Recognition Algorithm Under Low Illumination Condition. In: Zhao, P., Ouyang, Y., Xu, M., Yang, L., Ren, Y. (eds) Advances in Graphic Communication, Printing and Packaging. Lecture Notes in Electrical Engineering, vol 543. Springer, Singapore. https://doi.org/10.1007/978-981-13-3663-8_37
Download citation
DOI: https://doi.org/10.1007/978-981-13-3663-8_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3662-1
Online ISBN: 978-981-13-3663-8
eBook Packages: EngineeringEngineering (R0)