Face Recognition Using Gabor Wavelet in MapReduce and Spark

Phan, Anh-Cang; Cao, Hung-Phi; Tran, Ho-Dat; Phan, Thuong-Cang

doi:10.1007/978-3-030-21803-4_77

Cited by 4 publications

(7 citation statements)

References 3 publications

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“…Traditional data processing platforms, with limited computational capacity and space for growth, are unable to handle large amounts of data for digital statistics and analysis. In [14], they used the Gabor wavelet approach as well as the MapReduce parallel computing approach to develop a facial recognition system. e MapReduce paradigm in the SparkContext was used to execute parallel processing at the extracting and recognizing steps.…”

Section: Related Workmentioning

confidence: 99%

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Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

Zhang¹

2022

Mobile Information Systems

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From standard software to crucial applications, face recognition (FR) is always at the core of many unique advances over the past 20 years. Big data is a rapidly growing collection of data. It is important in many applications such as medical, academic, and industries, and it refers to vast data sources that are hard to analyze, store, and interpret for subsequent procedures. Hadoop is an open-source large data processing platform that stores and analyses data in scalable computer clusters. FR technology is built on Hadoop processing to boost the effectiveness of recognition using MapReduce computing. This paper presents a novel adaptive fine-tuned AdaBoost (AFTA) algorithm to enhance the FR in the Hadoop processing. Collected face data sets are employed in preprocessing stage to normalize and enhance the superiority through the median filter (MF) and contour-based image enhancement (CIE), correspondingly. To handle vast quantities of the data, boosted k-means clustering (BKC) approach is used over the Hadoop servers for the MapReduce process. The binary partition tree (BPT) approach is employed in the segmentation stage to split the data into many subgroups. To lessen the dimension of the data, we use the Gabor filter. To select the consistent features, threshold-based binary particle swarm optimization (T-BPSO) is applied. Then, our proposed technique is utilized in the FR process. Finally, the performance metrics of the proposed technique are examined and compared with existing techniques to accomplish our research with the greatest effectiveness. For accuracy, precision, recall, and F-score, we attained the proposed (AFTA) values of 99.67, 97.13, 95.21, and 94.11, respectively. The outcomes are depicted in graphical representation by employing the MATLAB tool.

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Section: Related Workmentioning

confidence: 99%

“…We are running Centos 7.3. e latest edition of Hadoop includes 2.6.5. Table 2 (14,12), 13)/dist (20,24) Table 3: F-score (%) of proposed and existing techniques.…”

Section: Performance Analysismentioning

confidence: 99%

Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

Zhang¹

2022

Mobile Information Systems

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“…In this paper, the computer CPU used in the experiment is Intel core i5-4200 m, the basic frequency is 2.5GHZ, and the internal storage capacity is 16GB. Phan A. C. et al (Phan et al, 2019) presented a face recognition system using the Gabor Wavelet method and the Map-Reduce parallel processing model. The authors built up a face recognition system with the help of Gabor filters to extract features of the face.…”

Section: Gabor Wavelet Transformmentioning

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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“…The eigenvectors of L= A associated to the eigenvalues are: L = (8) Such as: A (9) And (10) Since C = A , the relation (10) becomes: C(A (11) From (7) and (11) we notice that A and are respectively the eigenvectors and eigenvalues λ of C:…”

Section: γ=mentioning

confidence: 99%

“…These factors and others make face recognition very difficult and represent a big challenge for researchers to propose algorithms in order to handle, the above-mentioned problems. Among the methods that have shown a great success in face recognition we cite: Gabor Wavelets [10], [11]. Multiple approaches based on this method have been developed such as: Gabor-Fisher Classifier (GFC) [12], Gabor Wavelet Network (GWN) [13], Gabor-Fisher [14], etc.…”

Section: Introductionmentioning

confidence: 99%

Face recognition of face images with hidden parts using Gabor wavelets and PCA

Ennaama

Benhida²,

Boulahoual³

2019

PEN

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Face Recognition is one of the biometric methods that has recently gained significant attention at the level of research and the scientific community. However, in some special cases, face recognition methods can be sensitive to illumination, facial expressions, aging, face orientation, pose variation and hidden parts of face images, which make recognition very difficult. In this paper, we are interested in the hidden parts of a face image specifically those hidden by facial hair and/or a hair style. We first tested the Eigenfaces method for these modified images; but this approach failed to recognize them. So we have proposed an algorithm that combines Gabor magnitude and phase and PCA. To evaluate the efficiency of our algorithm, we used a variety of face images of FEI Brazilian database and hid the hairstyle and facial hair (barbs) to recognize them from the database images. The obtained results show that the proposed method (Gabor filter and PCA) attained high efficiency in the recognition for this type of problem.

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Face Recognition Using Gabor Wavelet in MapReduce and Spark

Cited by 4 publications

References 3 publications

Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

Hadoop Small Image Processing Technology Based on Big Data Processing and Its Application Effect in Face Feature Extraction and Face Recognition System Design

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

Face recognition of face images with hidden parts using Gabor wavelets and PCA

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