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

Raghavan, Jayanthi; Ahmadi, Majid

doi:10.1007/978-3-030-73103-8_51

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the image I (x, y) with discrete k. gray values, histogram is defined by the probability of occurrence of the gray level I [27], given by Equation (1) as follows:…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJAIA

View full text Add to dashboard Cite

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.

show abstract

“…For the image I (x, y) with discrete k. gray values, histogram is defined by the probability of occurrence of the gray level I [27], given by Equation (1) as follows:…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJAIA

View full text Add to dashboard Cite

show abstract

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

Cited by 1 publication

References 17 publications

Untitled

Untitled

Contact Info

Product

Resources

About