PCAPooL: unsupervised feature learning for face recognition using PCA, LBP, and pyramid pooling

Alahmadi, Amani; Hussain, Muhammad; Aboalsamh, Hatim

doi:10.1007/s10044-019-00818-y

Cited by 17 publications

(5 citation statements)

References 31 publications

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“…For example, when M =8, the value of LBP code becomes 256. Furthermore, it is also confirmed from[3,22,37] that the best performance is achieved using (8, 1) LBP parameters. As a result, the next simulations are executed using same optimal values of LBP parameters i.e.…”

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confidence: 61%

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Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images. Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have been proposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously. However, a more efficient method is still in demand to meet the day-today challenges to detect these forgeries at the same time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binary pattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractional parameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively. Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and spliced images. Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas in the image. Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE, and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively. To validate the effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms of ROC, precision, recall, F1 score, F2 score, and accuracy. Moreover, the robustness of the proposed work is tested under rotation attack and better results are attained in comparison to the existing techniques.

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confidence: 61%

“…M is total pixels in the circular neighborhood, and N is its radius [22,37]. In LBP, for 3×3 image blocks, each central pixel (m c ) is compared with its eight neighbors (m i , i = 0 : M − 1 ).…”

Section: Simulation Resultsmentioning

confidence: 99%

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Kaur¹,

Jindal²,

Singh³

2021

Turk J Elec Eng & Comp Sci

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“…The hardware used for experiments is based on the CPU image of Intel Xeon CPU E5-2620 v2@2.lO GHz, GPU for Nvidia Tesla K20m, 5G memory, and 32G memory. Alahmadi A. et al (Alahmadi et al, 2019) presented a novel unsupervised feature learning method PCAPooL for a face recognition system. This paper combines PCA, local binary pattern (LBP), and pyramid pooling.…”

Section: Pca (Principle Component Analysis)mentioning

confidence: 99%

A State-of-the-Art Survey on Face Recognition Methods

Modi¹,

Patel²

2021

International Journal of Computer Vision and Image Processing

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Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

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“…Local binary pattern (LBP) (Alahmadi et al , 2019) is a kind of local texture feature descriptor, which generates a descriptor for each pixel by calculating the relationship between each pixel and the neighboring pixels. The descriptor shows the local texture features around the pixel.…”

Section: Texture Feature Extraction Algorithm For Face Recognitionmentioning

confidence: 99%

A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

Wei

2020

IJICC

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PurposeIn order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.Design/methodology/approachThe algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.FindingsCompared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.Originality/valueThe proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

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PCAPooL: unsupervised feature learning for face recognition using PCA, LBP, and pyramid pooling

Cited by 17 publications

References 31 publications

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

Eﬀicient hybrid passive method for the detection and localization of copy-move and spliced images

A State-of-the-Art Survey on Face Recognition Methods

A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

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