Handling Illumination Variation: A Challenge for Face Recognition

Koringa, Purvi A.; Mitra, Suman K.; Asari, Vijayan K.

doi:10.1007/978-981-10-2107-7_25

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The shadows produced in facial images due to variation in lighting directions may cause loss of important facial features which are helpful for recognition. A preprocessing method must enhance the intensity in the regions of inadequately illuminated and decrease the intensity in the densely illuminated regions while retaining the intensity in the fairly illuminated portions [37]. Few important preprocessing techniques are discussed below.…”

Section: Preprocessing Methodsmentioning

confidence: 99%

A Modified CNN-Based Face Recognition System

Raghavan¹,

Ahmadi²

2021

IJAIA

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In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%.

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Section: Preprocessing Methodsmentioning

confidence: 99%

A Modified CNN-Based Face Recognition System

Raghavan¹,

Ahmadi²

2021

IJAIA

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“…Moreover, LBP is sensitive to local illumination variations and is no longer able to correctly perceive the texture of facial muscle tissue and different sorts of local deformations. In [13] .et al suggests how to handle the illumination variation for face images.…”

Section: Introductionmentioning

confidence: 99%

Modular FER: A Modular Facial Expression Recognition from Image Sequence Based on Two Dimensional (2D) Taylor Expansion

Sujata

Mitra

2021

SN COMPUT. SCI.

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This article proposes a robust facial expression recognition system, which involves two main contributions: (1) Modular approach (2) Two Dimensional Taylor Expansion for the facial feature extrication. In this facial expression recognition system (FER), the Logarithm-Laplace (LL) is used to handle illumination variation present in the face image. The modular approach mimics the human brain's capability to identify a person with limited facial regions.This article experimentally shows that some facial regions like eyes, nose, lips, and forehead contribute more to the expression recognition task. We also introduce 2D Taylor Feature Pattern (TFP) in light of the proposed Two Dimensional Taylor Expansion. Facial expression recognition's overall performance of the proposed mannequin is examined on some benchmark databases using the holistic approach and modular approach. Better recognition performance is achieved with the proposed model compared to the other existing approaches.

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Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques

Raghavan

Ahmadi

2021

Advances in Intelligent Systems and Computing

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Handling Illumination Variation: A Challenge for Face Recognition

Cited by 5 publications

References 11 publications

A Modified CNN-Based Face Recognition System

A Modified CNN-Based Face Recognition System

Modular FER: A Modular Facial Expression Recognition from Image Sequence Based on Two Dimensional (2D) Taylor Expansion

Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques

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