Nonlinear image enhancement to improve face detection in complex lighting environment

Li Tao; Ming-Jung Seow; Vijayan K. Asari

doi:10.1117/12.643262

3 April 2006 Nonlinear image enhancement to improve face detection in complex lighting environment

Li Tao, Ming-Jung Seow, Vijayan K. Asari

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 606416 (2006) https://doi.org/10.1117/12.643262
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

A robust and efficient image enhancement technique has been developed to improve the visual quality of digital images that exhibit dark shadows due to the limited dynamic ranges of imaging and display devices which are incapable of handling high dynamic range scenes. The proposed technique processes images in two separate steps: dynamic range compression and local contrast enhancement. Dynamic range compression is a neighborhood dependent intensity transformation which is able to enhance the luminance in dark shadows while keeping the overall tonality consistent with that of the input image. The image visibility can be largely and properly improved without creating unnatural rendition in this manner. A neighborhood dependent local contrast enhancement method is used to enhance the images contrast following the dynamic range compression. Experimental results on the proposed image enhancement technique demonstrates strong capability to improve the performance of convolutional face finder compared to histogram equalization and multiscale Retinex with color restoration without compromising the false alarm rate.

Citation Download Citation

Li Tao, Ming-Jung Seow, and Vijayan K. Asari "Nonlinear image enhancement to improve face detection in complex lighting environment", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 606416 (3 April 2006); https://doi.org/10.1117/12.643262

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available