Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination

Zhang, Le-Bing; Peng, Fei; Qin, Le; Long, Min

doi:10.1016/j.jvcir.2018.01.001

Cited by 68 publications

(27 citation statements)

References 25 publications

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“…Recently, person-specific methods [39,8] are proposed to improve the generalization ability of micro-texture based algorithms. Zhang et al [40] apply the Markov model on color texture features then conduct recursive feature elimination for face PAD. Boulkenafet et al [7] focus on luminance and chrominance channels where the joint information of color and texture is exploited.…”

Section: Related Workmentioning

confidence: 99%

Discriminative representation combinations for accurate face spoofing detection

Song

Zhao

Fang

et al. 2019

Pattern Recognition

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Three discriminative representations for face presentation attack detection are introduced in this paper. Firstly we design a descriptor called spatial pyramid coding micro-texture (SPMT) feature to characterize local appearance information. Secondly we utilize the SSD, which is a deep learning framework for detection, to excavate context cues and conduct end-to-end face presentation attack detection. Finally we design a descriptor called template face matched binocular depth (TFBD) feature to characterize stereo structures of real and fake faces. For accurate presentation attack detection, we also design two kinds of representation combinations. Firstly, we propose a decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a simple score fusion strategy to combine face structure cues (TFBD) with local micro-texture features (SPMT). To demonstrate the effectiveness of our design, we evaluate the representation combination of SPMT and SSD on three public datasets, which outperforms all other state-of-the-art methods. In addition, we evaluate the representation combination of SPMT and TFBD on our dataset and excellent performance is also achieved.

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Section: Related Workmentioning

confidence: 99%

Discriminative representation combinations for accurate face spoofing detection

Song

Zhao

Fang

et al. 2019

Pattern Recognition

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“…Despite limited solutions, a methodology based on machine learning [ 36 , 57 , 59 , 114 ], medical imaging [ 102 ], fusion and oncology, Natural language processing [ 118 ], and different learning algorithms [ 14 , 32 , 48 – 56 , 75 , 76 , 81 , 87 , 119 – 121 , 125 ] could be used for measuring the coronavirus COVID-19 disease.…”

Section: Classification Of Key Areamentioning

confidence: 99%

Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

Bhargava

Bansal

2021

Multimed Tools Appl

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The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.

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“…This subsection shows of our framework's evaluation. We applied KDDTest+ and KDDTest-21 datasets and following different machine learning classifiers: Naive Bayes (NB) [34,35], Logistic Regression (LR) [36,37], Jrip (JR) [38], J48 Decision Tree (J48) [39], LMT Decision Tree (LMT), Random Forest (RF), Support Vector Machine (SMO) [40][41][42], K-Nearest Neighbors (IBK) [43,44]. All classifier machine learning methods are notified in Table 5.…”

Section: Proposed Framework Evaluationmentioning

confidence: 99%

Machine learning based distributed big data analysis framework for next generation web in IoT

et al. 2021

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For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

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Face spoofing detection based on color texture Markov feature and support vector machine recursive feature elimination

Cited by 68 publications

References 25 publications

Discriminative representation combinations for accurate face spoofing detection

Discriminative representation combinations for accurate face spoofing detection

Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

Machine learning based distributed big data analysis framework for next generation web in IoT

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