Review of Technical Approaches to Face Recognition with Varying Pose and Illumination in Unconstrained Scenes

Fagbola, Temitayo Matthew; Olabiyisi, Stephen O.; Egbetola, Funmilola I; Oloyede, Ayodele

doi:10.46792/fuoyejet.v2i1.48

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Feature extraction is a major dimensionality reduction step to ensure the accuracy of the classification and improved time efficiency (Fagbola et al, 2012;Fagbola et al, 2017). In this paper, relevant features were extracted from the terms returned via the pre-processing step using a normalized TF-IDF technique.…”

Section: Feature Extractionmentioning

confidence: 99%

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

Fagbola¹,

Thakur²,

Olugbara³

2019

IJTech

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News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter's algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency-Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.

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Section: Feature Extractionmentioning

confidence: 99%

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

Fagbola¹,

Thakur²,

Olugbara³

2019

IJTech

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“…Also, [4] said face recognition from unconstrained scenes has been a subject of debate among researchers as a result of the massive influx of video surveillance system (VSS) and other ubiquitous hand-held video capturing devices.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Principal Component Analysis and Kernel Principal Component Analysis Using TOAM Database

Madandola

Gbolagade

Wuraola

2019

AJRCoS

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Face recognition algorithms can be classified into appearance-based (Linear and Non-Linear Appearance-based) and Model-based Algorithms. Principal Component Analysis (PCA) is an example of Linear Appearance-based which performs a linear dimension reduction while Kernel Principal Component Analysis (KPCA) is an example of non-linear appearance methods. The study focuses on the performance assessment of PCA and KPCA face recognition techniques. The assessment is carried out base on computational time using testing time and recognition accuracy on created database identified as TOAM database. The created database is mainly for this research purpose and it contains 120 face images of 40 persons frontal faces with 3 images of each individual under different lighting, facial expressions, occulations, environment and time. The findings reveal an average testing Time of 1.5475 seconds for PCA and 67.0929 seconds for KPCA indicating a longer Computational time for KPCA than PCA. It also reveals that PCA has 72.5% performance recognition accuracy while KPCA has 80.0% performance recognition accuracy indicating that KPCA outperforms the PCA in terms of recognition accuracy.

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Investigation of face recognition using Gabor filter with random forest as learning framework

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Noor

Low

et al. 2017

TENCON 2017 - 2017 IEEE Region 10 Conference

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Review of Technical Approaches to Face Recognition with Varying Pose and Illumination in Unconstrained Scenes

Cited by 4 publications

References 20 publications

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

Performance Assessment of Principal Component Analysis and Kernel Principal Component Analysis Using TOAM Database

Investigation of face recognition using Gabor filter with random forest as learning framework

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