Face Recognition Under Varying Illumination

Lian, Zhichao; Er, Meng Joo

doi:10.5772/10413

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…In recent times, automatic face recognition systems obtained the highest accuracy incontrolled environments. Yet, there are challenges that need to be addressed in uncontrolled environments [20]. Illumination variation is one of the greatchallenges that must be handled properly to achievehigher accuracy in the FR system.…”

Section: Related Workmentioning

confidence: 99%

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Untitled

2023

IJAIA

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Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of pixels.There are manychallenges that affect the performance of face recognitionincluding illumination variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification methods, experiments involving various block sizes are conducted to assess the computation performance and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2 block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function performs extremely well on theAT&Tdataset for both holistic and block-based approaches.

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Section: Related Workmentioning

confidence: 99%

“…Various approaches have been recommended to resolve this problem. In this paper, a few preprocessing methods to handle the illumination variation problem are suggested [20].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJAIA

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“…Illumination changes occur frequently in real face images and hence it is imperative to develop identification methods that are robust to these changes. [9] and [10] contain comprehensive surveys and reviews of current face recognition methods that work well under varying illumination conditions. [11] proposed an appearancebased algorithm for face recognition across pose by estimating the eigen light-field from a collection of images.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Models for Human Face Recognition under Illumination Variations

Mitra¹

2012

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The appearance of a face is severely altered by illumination conditions that makes automatic face recognition a challenging task. In this paper we propose a Gaussian Mixture Models (GMM)-based human face identification technique built in the Fourier or frequency domain that is robust to illumination changes and does not require "illumination normalization" (removal of illumination effects) prior to application unlike many existing methods. The importance of the Fourier domain phase in human face identification is a well-established fact in signal processing. A maximum a posteriori (or, MAP) estimate based on the posterior likelihood is used to perform identification, achieving misclassification error rates as low as 2% on a database that contains images of 65 individuals under 21 different illumination conditions. Furthermore, a misclassification rate of 3.5% is observed on the Yale database with 10 people and 64 different illumination conditions. Both these sets of results are significantly better than those obtained from traditional PCA and LDA classifiers. Statistical analysis pertaining to model selection is also presented.

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