Abstract

This paper concerns with stochastic resonance for face detector that detects the face from the dark static images under different illumination conditions. The face candidates of the dark images are very hard to identify as they are not clearly visible. In this paper, we have designed the algorithm by combining the stochastic resonance and our high performance face detector presented previously. We are applying our SR for image processing in a dark image for extracting the face candidates from the image to make it visible. The Gaussian white noise is applied on the dark image and the images are summed up in a series for extracting the image candidates from the dark images and for making it visible. This process is also known as the summing network of the stochastic resonance noise. The dark image once made visible is applied to our face detector for detecting the presence of the face in an image. The use of stochastic resonance in a face detection algorithm for the static dark images reflects the novelty of our paper. We performed various experiments on various dark images under different illumination conditions and confirmed the effectiveness of SR for detecting the faces in a dark image.

Highlights

  • The physiological biometric identifier that is widely used in human recognition is the face

  • In order to validate our proposed algorithm, we performed some experiments on different dark images under different illumination conditions

  • We developed a C programming code for checking the efficiency of different stochastic resonance (SR) techniques and draw a matrix table showing for the possibility of the face detection on different images

Read more

Summary

Introduction

The physiological biometric identifier that is widely used in human recognition is the face. We test our algorithm on two different techniques of SR, one is the threshold technique (6), where the noise is tuned along with a threshold In this case, after the Gaussian white noise has been added to the dark and the illumination variant image. We are applying Gaussian white noise on a dark and illumination variant image and sum it up with the original image. Because in the case of image processing, the pixel series of the images are calculated instead of time series This makes it possible to protect the image from losing the special features which are necessary to detect faces in an image. We used a few illumination variant images from the INRIA Graz-01 (8), and FDDB database (9) which could not be detected by our high performance face detector (1).

Stochastic Resonance for Image processing
Original SR technique
SR without tuning
Our SR technique for image processing
Why “our SR technique for image processing”?
Image enhancement phase
Preparation for experiments
Databases used
Evaluation of different image cases
Discussions
Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.