Illumination Invariant Face Recognition

Abstract

Face Recognition is a term that includes several subproblems. Though face recognition have been a grown up research area, however, there still remain many problems that must be overcome to develop a robust face recognition system that works well under various circumstances such as illumination, pose and expressions variations. Such variations have proven to be one of the biggest problems of face recognition systems. In the proposed thesis work the problem of illumination is discussed. A method based on the combination of Retinex and LOG-DCT technique is applied to suppress the illumination and for better face recognition results. After illumination normalization LBP is used for extracting the features of normalized images, which are further used for face recognition. In the proposed method the experiments on Extended Yale B database show that by using the proposed method better recognition performance and results can be obtained.

Illumination-Invariant Face Recognition in Hyperspectral Images

“…The Quotient Image [5] uses the relative reflectance with respect to model images to represent a subject. Retinexbased approaches [6,7,11] aim to remove the illumination effect by estimating the illumination through filtering. Transformation-based methods [8][9][10][11] use phase or other…”

“…For example, a variety of subspace-based methods have been proposed [3,4]. Methods based on illumination-invariant features have also been proposed [5][6][7][8][9][10][11][12][13][14]. On the other hand, previous studies have shown that spectral information is useful for this purpose [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Unconstrained Face Recognition Quality: A Review

“…We in work prevalent results on regular encounter requirements, along with a unique "in the wild" described dataset using the purpose of suggest our plan continue their stateof-the-art, from time to time somewhat [51]. In the prepared analysis work a technique on the basis of the group of Retinex and RECORD (DCT) plan is set up.…”

Multi Featured Fuzzy based Block Weight Assignment and Block Frequency Map Model for Transformation Invariant Facial Recognition

“…Author [8] employs the same engine feature space to reach the recognition in case of pose difference. Some other feature vectors applied by different researchers for improving the recognition accuracy in case of multiple feature difference parameters includes DCT, Nearest Neighbor Discriminant Analysis [10], LBP [12], Gabor Filter [13], textural feature [18] etc. These features are used individually or collectively for improving the recognition accuracy in case of head movement [14], pose variation [15], illumination [16][17] variant, age variation etc.…”