High-dimensional feature extraction using bit-plane decomposition of local binary patterns for robust face recognition

Yoo, Cheol-Hwan; Kim, Seung Wook; Jung, June-Young; Ko, Sung-Jea

doi:10.1016/j.jvcir.2017.02.009

Cited by 22 publications

(11 citation statements)

References 27 publications

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“…Then complaining all bit planes to generate the feature vector. The experimental results showed that this method achieve high performance compared with the existing methods [38].…”

Section: Literature Reviewmentioning

confidence: 86%

“…This paper focusing on that the existing face recognition and classification methods concentrated on characterize the representation error.This approach is implemented using nuclear norm to describe the low-rank structural information, on the other hand this may leads to suboptimal solution. This approach leads to an optimal results [37].C-H. Yoo et al (2017), improved an effective feature extraction method for classifying images. This method realizeand improve the ability for face recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Human Identification via Face Recognition: Comparative Study

Prasad¹,

Al-Ani²,

Nejres³

2017

IOSR JCE

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“…Then complaining all bit planes to generate the feature vector. The experimental results showed that this method achieve high performance compared with the existing methods [38].…”

Section: Literature Reviewmentioning

confidence: 86%

Section: Literature Reviewmentioning

confidence: 99%

Human Identification via Face Recognition: Comparative Study

Prasad¹,

Al-Ani²,

Nejres³

2017

IOSR JCE

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“…Jung, and S.J. Ko [16] developed a new feature extraction approach by utilizing the high dimensional feature space for enhancing the discriminative ability for face recognition. At first, the local binary pattern was decomposed into several bit planes that consists of scale specific directional information of the facial images.…”

Section: Literature Surveymentioning

confidence: 99%

“…The distance between the fire-flies and in the points and is mathematically represented in the Eqs. (15) and (16) on the basis of Chi square distance measure.…”

Section: Feature Optimizationmentioning

confidence: 99%

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Prakash¹

2020

IJIES

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Face recognition is an emerging research area in biometric identification systems, which attracted many researchers in pattern recognition and computer vision. Since, the human facial images are high dimensional in nature, so a new optimization algorithm was proposed in this paper to reduce the dimension of facial images. At first, the input facial images were collected from ORL, YALE, and FAce Semantic SEGmentation (FASSEG) databases. Then, feature extraction was then performed by using Local Ternary Pattern (LTP) and Binary Robust Invariant Scalable Key points (BRISK) to extract the features from the face images. In addition, an enhanced fire-fly optimizer was used to reject the irrelevant features or to reduce the dimension of the extracted features. In enhanced fire-fly optimization algorithm, Chi square distance measure was used to find the distance between the fire-flies and also it uses only a limited number of feature values for representing the data that effectively reduces the "curse of dimensionality" concern. Finally, Deep Belief Network (DBN) was used to classify the individual's facial images. The experimental outcome showed that the proposed work improved recognition accuracy up to 7-20% compared to the existing work.

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“…They were assigned empirically, without substantiation, although the gain from applying the updated TS (2) for training the 2LP on STSM6080I NDPD as a model of STSOs with a normally distributed feature distortion (NDFD) was obvious [3], [4], [7], [10], [11]. Let the post-training configuration of 2LP (6) be denoted by .…”

Section: Update Of Ts (1) For Training On Stsosmentioning

confidence: 99%

Classifying Scaled-Turned-Shifted Objects with Optimal Pixel-to-Scale-Turn-Shift Standard Deviations Ratio in Training 2-Layer Perceptron on Scaled-Turned-Shifted 4800-Featured Objects under Normally Distributed Feature Distortion

Romanuke

2017

Electrical, Control and Communication Engineering

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-The problem of classifying diversely distorted objects is considered. The classifier is a 2-layer perceptron capable of classifying greater amounts of objects in a unit of time. This is an advantage of the 2-layer perceptron over more complex neural networks like the neocognitron, the convolutional neural network, and the deep learning neural networks. Distortion types are scaling, turning, and shifting. The object model is a monochrome 60 × 80 image of the enlarged English alphabet capital letter. Consequently, there are 26 classes of 4800-featured objects. Training sets have a parameter, which is the ratio of the pixel-to-scale-turn-shift standard deviations, which allows controlling normally distributed feature distortion. An optimal ratio is found, at which the performance of the 2-layer perceptron is still unsatisfactory. Then, the best classifier is further trained with additional 438 passes of training sets by increasing the training smoothness tenfold. This aids in decreasing the ultimate classification error percentage from 35.23 % down to 12.92 %. However, the expected practicable distortions are smaller, so the percentage corresponding to them becomes just 1.64 %, which means that only one object out of 61 is misclassified. Such a solution scheme is directly applied to other classification problems, where the number of features is a thousand or a few thousands by a few tens of classes.

show abstract

High-dimensional feature extraction using bit-plane decomposition of local binary patterns for robust face recognition

Cited by 22 publications

References 27 publications

Human Identification via Face Recognition: Comparative Study

Human Identification via Face Recognition: Comparative Study

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Classifying Scaled-Turned-Shifted Objects with Optimal Pixel-to-Scale-Turn-Shift Standard Deviations Ratio in Training 2-Layer Perceptron on Scaled-Turned-Shifted 4800-Featured Objects under Normally Distributed Feature Distortion

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