2016_Feature Reduction Techniques To Enhance Face Recognition
| Format: | General Document |
|---|
| _version_ | 1860798151074512896 |
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| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2016-08-07 |
| format | General Document |
| id | 16193 |
| institution | UniSZA |
| originalfilename | FEATURE REDUCTION TECHNIQUES TO ENHANCE FACE RECOGNITION.pdf |
| person | Zahradden Sufyanu |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16193 |
| sourcemedia | Server storage Scanned document |
| spelling | 16193 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16193 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access UNIVERSITI SULTAN ZAINAL ABIDIN SAMBox 2.4.24; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) Copyright©PWB2025 287 Face—Recognition 2016-08-07 FEATURE REDUCTION TECHNIQUES TO ENHANCE FACE RECOGNITION.pdf Face—Recognition Zahradden Sufyanu 2016_Feature Reduction Techniques To Enhance Face Recognition Face processing provides a user-friendly and nonintrusive means of human identificafion. The fundamental problems in face identification are uncontrolled illumination conditions and high degree of features complexity. Many studies focus on illumination normalization and dimensionality reduction. Yet, simplistic and realistic criteria are demanded. ln this thesis, a fiamework that enhances feature selection for simultaneous detection and recognition of face using Hough Transform (HT) is introduced. Subsequently, Anisotropic Diffusion Illumination Normalization technique based DCT (ASDCT) is proposed to compensate illumination and improve the decorrelation of features. Meanwhile, an algorithm named 'lntegrated Histograms based on Discrete Cosine Transform' (HIDCT) is developed to increase data compaction and reduce redundancy. Reduced number of features is obtained through rotating histogram plots by 30 degrees. Then HT maffices are extracted for face recognition. The best illumination normalization technique is known by comparing 22 illumination normalization techniques in the literature. Then 19 coefficients with largest magnitude are selected. Discrete Cosine Transform (DCT) is exploited on the reduced features to form new coefficients that optimize lossy compression techniques. The performance of the system is tested using standard research databases. Template matching and nearest neighbor classifiers are used for similarity measuements. Experimental results indicated that, the enhanced framework is very effective and accomplished an average detection time of 1.199 seconds, and minimum recognition accuracy of 95.6250/o. ASDCT technique showed up to 97.700o/o veification rate at I .0% of False Acceptance Rate (FAR). This supersedes Appearance based techniques such as Linear Discriminant Analysis (LDA), and Kemel Principal Component Analysis (KPCA). The level of compression of HIDCT algorithm is at least three times the conventional DCT. The proposed HIDCT is simple to implement and can accommodate large users within a small memory space. Furthermore, the tkee fiameworks encourage real world applications for efficient identifications. Dissertations, Academic Facial Recognition Systems Feature Reduction Thesis |
| spellingShingle | 2016_Feature Reduction Techniques To Enhance Face Recognition |
| state | Terengganu |
| subject | Face—Recognition Dissertations, Academic |
| summary | Face processing provides a user-friendly and nonintrusive means of human identificafion. The fundamental problems in face identification are uncontrolled illumination conditions and high degree of features complexity. Many studies focus on illumination normalization and dimensionality reduction. Yet, simplistic and realistic criteria are demanded. ln this thesis, a fiamework that enhances feature selection for simultaneous detection and recognition of face using Hough Transform (HT) is introduced. Subsequently, Anisotropic Diffusion Illumination Normalization technique based DCT (ASDCT) is proposed to compensate illumination and improve the decorrelation of features. Meanwhile, an algorithm named 'lntegrated Histograms based on Discrete Cosine Transform' (HIDCT) is developed to increase data compaction and reduce redundancy. Reduced number of features is obtained through rotating histogram plots by 30 degrees. Then HT maffices are extracted for face recognition. The best illumination normalization technique is known by comparing 22 illumination normalization techniques in the literature. Then 19 coefficients with largest magnitude are selected. Discrete Cosine Transform (DCT) is exploited on the reduced features to form new coefficients that optimize lossy compression techniques. The performance of the system is tested using standard research databases. Template matching and nearest neighbor classifiers are used for similarity measuements. Experimental results indicated that, the enhanced framework is very effective and accomplished an average detection time of 1.199 seconds, and minimum recognition accuracy of 95.6250/o. ASDCT technique showed up to 97.700o/o veification rate at I .0% of False Acceptance Rate (FAR). This supersedes Appearance based techniques such as Linear Discriminant Analysis (LDA), and Kemel Principal Component Analysis (KPCA). The level of compression of HIDCT algorithm is at least three times the conventional DCT. The proposed HIDCT is simple to implement and can accommodate large users within a small memory space. Furthermore, the tkee fiameworks encourage real world applications for efficient identifications. |
| title | 2016_Feature Reduction Techniques To Enhance Face Recognition |
| title_full | 2016_Feature Reduction Techniques To Enhance Face Recognition |
| title_fullStr | 2016_Feature Reduction Techniques To Enhance Face Recognition |
| title_full_unstemmed | 2016_Feature Reduction Techniques To Enhance Face Recognition |
| title_short | 2016_Feature Reduction Techniques To Enhance Face Recognition |
| title_sort | 2016_feature reduction techniques to enhance face recognition |