A biometric approach towards recognizing face in various dark illuminations

Zeenathunisa, S.; Jaya, A.; Rabbani, Munir Ahamed

doi:10.1109/icecct.2011.6077059

Cited by 3 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In image processing step [1,2], noise plays a vital problem. Noise is a major factor in degradation of the quality of images.…”

Section: Introductionmentioning

confidence: 99%

Additive and Multiplicative Noise Removal by using Gradient Histogram Preservations Approach

Roy¹,

Jain²

2015

IJCA

View full text Add to dashboard Cite

Image denoising is a traditional yet essential issue in low level vision. Existing denoising technique denoise image but these techniques doesn't concern about multiplicative noise removals. Due to that image texture are not preserved and PSNR value does not properly improved. Image denoising technique uses a novel Gradient Histogram Preservation (GHP) algorithm which preserves image quality. Presently, this technique denoises only additive noise removal. It cannot be applied to non-additive removal, such as multiplicative, Poisson noise and signalindependent noise and it also takes more time in calculations. Since both the noises are dissimilar in nature therefore it is difficult to eliminate both the noises by using single filter. To solve the above issue ,in this paper a novel GHP approach is used to remove additive white Gaussian noise (AWGN) effectively. Since speckle noise is multiplicative in nature; it is converted into additive noise by logarithmic transformation method before apply GHP algorithm. In this paper we use the approach that is to acquire a logarithmic transformation, calculate a covariance matrix of the transformed data, generate random number which follows mean zero and variance/covariance c times the variance/covariance computed in the previous step, then take antilog of the normalized data and apply novel technique using, Fast Fourier Transfer (FFT), Gaussian filter, local content metrics texture ,Iterative Histogram Specifications (IHS) which can denoise both types of noise removal, additive and non-additive noise removal and also takes less calculation time.. In image processing FFT is used in a wide variety of applications, like image analysis, image reconstruction, image filtering and image compression . Gaussian separating is utilized to obscure pictures and evacuate clamor. The proposed algorithm offers to remove the multiplicative noise and improves the visual quality of images.

show abstract

“…In image processing step [1,2], noise plays a vital problem. Noise is a major factor in degradation of the quality of images.…”

Section: Introductionmentioning

confidence: 99%

Additive and Multiplicative Noise Removal by using Gradient Histogram Preservations Approach

Roy¹,

Jain²

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…These methods will perform well when the test image is noise free. So, Noise plays vital role in image processing stage [2] [3].…”

Section: Introductionmentioning

confidence: 99%

“…In order to identify faces in an image, images are enhanced by removing noise in the image with preserving important information of the image. These noises are generally affected the quality of the image and formed during images captured from the sources like sensor, digital camera, CCTV, stored in storage media [3]. There are two types of images: gray scale image and color image.…”

Section: Introductionmentioning

confidence: 99%

Survey on Noise Removal in Digital Images

Jabarullah¹

2012

IOSRJCE

View full text Add to dashboard Cite

When an Image is formed, factors such as lighting (spectra, source, and intensity) and camera characteristics (sensor response, lenses) effect the appearance of the image. So, the prime factor that reduces the quality of the image is Noise. Noise hides the important details of images. To enhance the image qualities, we have to remove noises from the images without loss of any image information. Noise removal is one of the pre-processing stages of image processing. There are different types of noises which corrupt the images. These noises are appeared on images in different ways: at the time of acquisition due to noisy sensors, due to faulty scanner or due to faulty digital camera, due to transmission channel errors, due to corrupted storage media. In order to get enhanced images, many researchers present several methods to remove noises from different types of images by preserving important details like structural features, textural information. In this paper, we present a survey on types of Noises, types of images and noise removal algorithms. We have considered three types of noises: Impulse noises, Speckle noise, Gaussian noise from two most useful images: sensor images, medical images and gray scale images. We analyze all noise removal algorithms for each noise from each of these images. At the end of our study, we present comparative study of all these algorithms and, conclude with several promising directions for future research work.

show abstract

“…Zeenathunisa et al, [7] proposed a bio metric identification system for frontal static face image subjected to various dark illu minations the framework has preprocessing method, local feature ext ractor and a recognizer. An automatic Face Recognition Bio metric System (FRBS) has been developed that uses Local Binary Pattern (LBP) and k -Nearest Neighbor classifier.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters

C¹,

reddy²,

Raja³

et al. 2017

IJERA

View full text Add to dashboard Cite

The Bio metrics is used to recognize a person effectively co mpared to trad itional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Co mplex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian dist ance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.

show abstract

A biometric approach towards recognizing face in various dark illuminations

Cited by 3 publications

References 9 publications

Additive and Multiplicative Noise Removal by using Gradient Histogram Preservations Approach

Additive and Multiplicative Noise Removal by using Gradient Histogram Preservations Approach

Survey on Noise Removal in Digital Images

Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters

Contact Info

Product

Resources

About