An Illumination Invariant Face Recognition by Enhanced Contrast Limited Adaptive Histogram Equalization

Thamizharasi, A; J.S, Jayasudha

doi:10.21917/ijivp.2016.0183

Cited by 5 publications

(3 citation statements)

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“…CLAHE permits to limit the over-amplification of noise components by modifying the slope of CDF function as expressed in Eq. ( 1) to a desired level by imposing a parameter known as clip limit (𝒞) [47]. The modified CDF function is denoted in Eq.…”

Section: Contrast Limited Adaptive Histogram Equalizationmentioning

confidence: 99%

Low Frequency DD-DWT based illumination compensated and color intensified features for Face Recognition

Chaudhary

Yadav

2023

Preprint

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The efficacy of face recognition systems is significantly affected by uneven light deviations, incident over images from different directions. This work presents a light invariable color face recognition method that efficiently normalizes illumination variances along with substantial improvement in color intensities. The proposed method includes following steps: Initially, segregation of facial images into primary spectral color components is performed to effectively balance contrast and evenly amplify color intensity levels. The varying illumination effects are due to low frequency component of image; therefore, Difference of Gaussian (DoG) high-pass filter based homomorphic filtering is further utilized in HSI (Hue, Saturation and Intensity) space. This conversion into HSI color space is mainly performed to ward off any colors distortion that may prevail in images due to direct utilization of further improvements on each color channel. Thus, this model aids in easily separating the chromaticity component from intensity part of an image. The normalized intensity component is concatenated with unaltered chromaticity components to effectively possess color facial information. Subsequently, extraction of double density discrete wavelet transform (DD-DWT) based coefficients of images is achieved for selection of substantial discriminative frequency components of face images. The large facial feature vector space is hereafter projected over eigen subspace to reduce dimensions effectively. In this work, k-nearest neighbor linear classifier has been utilized to classify selective features. The effectiveness of investigated method has been assessed on AR and CMU-PIE color face databases and attained results are thus contrasted with prior techniques which benefit in establishing its superiority.

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Section: Contrast Limited Adaptive Histogram Equalizationmentioning

confidence: 99%

Low Frequency DD-DWT based illumination compensated and color intensified features for Face Recognition

Chaudhary

Yadav

2023

Preprint

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“…Researchers used Contrast Limited Adapted Histogram Equalization (CLAHE), which used popularly in medical images, to create face recognition system for variant illumination conditions [6]. They achieved this method by enhancing (CLAHE) technique and called their introduced method "Enhanced CLAHE".…”

Section: Related Workmentioning

confidence: 99%

“…Results of CLAHE was compared with the Enhanced CLAHE and the Enhanced CLAHE efficiency was tested with three common databases, Yale, ORL and AR. In the future, they looked to reduce features dimensions and increase the accuracy of face recognition by using SVM classifier [6].…”

Section: Related Workmentioning

confidence: 99%

Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement

Elaw¹

2020

AJCST

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Face detection is a common computer technology being used in human identification applications. It can also refer to the process of locating human faces in a visual scene. Face detection is a branched field of object detection where all objects in an image are detected including several classes like cars, trees, humans… etc. Also face detection problems branch into a lot of cases, some focus on frontal faces, others focus on side pose and so on. In this paper, a new face detection method based on Bilinear Interpolation image zooming method and image enhancement by Adaptive Histogram Equalization (AHE) method is proposed. The new method gives an encouraging results for crowded human images. By comparing the proposed method with the Viola-Jones algorithm, face detector using the cascade object detector, which supported in MATLAB, the new method gives excellent results in detecting human faces with different resolutions, poses and sizes. It succeeds in detecting most of the human faces in the tested images regardless of image sizes. The new method is tested on several images in Pratheepan dataset with crowded humans. Also, I tested the new method on many images collected from the Internet, whose can be classified as crowded human images. Experimental results show that the proposed Ad_L_Hist method is more efficient in detecting human faces in crowded human images.

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ST-VLAD: Video Face Recognition Based on Aggregated Local Spatial-Temporal Descriptors

2021

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How to integrate the temporal and spatial continuity information, when designing the video texture description operator, is crucial to realize video face recognition and facilitate video analysis and understanding, however, it has still yet to be properly addressed. In this paper, a novel video face recognition algorithm is proposed based on an aggregated local spatial-temporal descriptor (ST-VLAD), followed by a novel Fisher Criterion-based weight-learning method, which portrays the local information of the video more accurately, therefore largely improving the representation ability of description vectors. The proposed descriptor was tested on two representative databases, Honda/UCSD and YouTube Face database, achieving accuracies of 89.7% and 87.3%, respectively. The proposed method greatly outperformed the other existing state-of-art methods, suggesting a potential broad utility in the field of video face recognition.

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An Illumination Invariant Face Recognition by Enhanced Contrast Limited Adaptive Histogram Equalization

Cited by 5 publications

References 0 publications

Low Frequency DD-DWT based illumination compensated and color intensified features for Face Recognition

Low Frequency DD-DWT based illumination compensated and color intensified features for Face Recognition

Face Detection in Crowded Human Images by Bi-Linear Interpolation and Adaptive Histogram Equalization Enhancement

ST-VLAD: Video Face Recognition Based on Aggregated Local Spatial-Temporal Descriptors

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