HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine

Khushwant Sehra; Ankit Rajpal; Anurag Mishra; Girija Chetty

doi:10.1007/978-3-030-24308-1_35

HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine

Khushwant Sehra, Ankit Rajpal, Anurag Mishra, Girija Chetty

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Extreme Learning Machine has attracted widespread attention for its exemplary performance in solving regression and classification problems. It is a type of single layer feed-forward neural machine which relies on randomly allocating the input weights and hidden layer biases. Through this, the ELM has been found to possess running time spans which are within millisecond regime. It does not require complex controlling parameters which makes its implementation elementary. This paper investigates the performance of employing Extreme Learning Machine as a classifier to be used for the face recognition problem. Viola Jones algorithm has been employed to detect and extract the faces from the dataset. Finally, Histogram of Oriented Gradients (HOG) features are extracted which form the basis of classification. The scheme so presented has been tested on standard face recognition datasets from AT&T and YALE. The resulting training/testing time spans of the whole scheme range from milliseconds to seconds, dictating the compatibility of ELM with real-time events.

Original language	English
Title of host publication	Computational Science and Its Applications – ICCSA 2019
Subtitle of host publication	19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V
Editors	Sanjay Misra, Osvaldo Gervasi, Beniamino Murgante, Elena Stankova, Vladimir Korkhov, Carmelo Torre, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan, Eufemia Tarantino
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	423-435
Number of pages	13
Volume	11623
ISBN (Electronic)	9783030243081
ISBN (Print)	9783030243074
DOIs	https://doi.org/10.1007/978-3-030-24308-1_35
Publication status	Published - 29 Jun 2019
Event	19th International Conference on Computational Science and Its Applications, ICCSA 2019 - Saint Petersburg, Russian Federation Duration: 1 Jul 2019 → 4 Jul 2019

Publication series

Name	Lecture Notes in Computer Science
Volume	11623
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computational Science and Its Applications, ICCSA 2019
Country/Territory	Russian Federation
City	Saint Petersburg
Period	1/07/19 → 4/07/19

Access to Document

10.1007/978-3-030-24308-1_35

Cite this

Sehra, K., Rajpal, A., Mishra, A., & Chetty, G. (2019). HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine. In S. Misra, O. Gervasi, B. Murgante, E. Stankova, V. Korkhov, C. Torre, A. M. A. C. Rocha, D. Taniar, B. O. Apduhan, & E. Tarantino (Eds.), Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V (Vol. 11623, pp. 423-435). (Lecture Notes in Computer Science; Vol. 11623). Springer. https://doi.org/10.1007/978-3-030-24308-1_35

Sehra, Khushwant ; Rajpal, Ankit ; Mishra, Anurag et al. / HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine. Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V. editor / Sanjay Misra ; Osvaldo Gervasi ; Beniamino Murgante ; Elena Stankova ; Vladimir Korkhov ; Carmelo Torre ; Ana Maria A.C. Rocha ; David Taniar ; Bernady O. Apduhan ; Eufemia Tarantino. Vol. 11623 Cham, Switzerland : Springer, 2019. pp. 423-435 (Lecture Notes in Computer Science).

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title = "HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine",

abstract = "Extreme Learning Machine has attracted widespread attention for its exemplary performance in solving regression and classification problems. It is a type of single layer feed-forward neural machine which relies on randomly allocating the input weights and hidden layer biases. Through this, the ELM has been found to possess running time spans which are within millisecond regime. It does not require complex controlling parameters which makes its implementation elementary. This paper investigates the performance of employing Extreme Learning Machine as a classifier to be used for the face recognition problem. Viola Jones algorithm has been employed to detect and extract the faces from the dataset. Finally, Histogram of Oriented Gradients (HOG) features are extracted which form the basis of classification. The scheme so presented has been tested on standard face recognition datasets from AT&T and YALE. The resulting training/testing time spans of the whole scheme range from milliseconds to seconds, dictating the compatibility of ELM with real-time events.",

keywords = "Machine Learning, face recognition, Image analysis",

author = "Khushwant Sehra and Ankit Rajpal and Anurag Mishra and Girija Chetty",

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Sehra, K, Rajpal, A, Mishra, A & Chetty, G 2019, HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine. in S Misra, O Gervasi, B Murgante, E Stankova, V Korkhov, C Torre, AMAC Rocha, D Taniar, BO Apduhan & E Tarantino (eds), Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V. vol. 11623, Lecture Notes in Computer Science, vol. 11623, Springer, Cham, Switzerland, pp. 423-435, 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Saint Petersburg, Russian Federation, 1/07/19. https://doi.org/10.1007/978-3-030-24308-1_35

HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine. / Sehra, Khushwant; Rajpal, Ankit; Mishra, Anurag et al.
Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V. ed. / Sanjay Misra; Osvaldo Gervasi; Beniamino Murgante; Elena Stankova; Vladimir Korkhov; Carmelo Torre; Ana Maria A.C. Rocha; David Taniar; Bernady O. Apduhan; Eufemia Tarantino. Vol. 11623 Cham, Switzerland: Springer, 2019. p. 423-435 (Lecture Notes in Computer Science; Vol. 11623).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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T1 - HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine

AU - Sehra, Khushwant

AU - Rajpal, Ankit

AU - Mishra, Anurag

AU - Chetty, Girija

PY - 2019/6/29

Y1 - 2019/6/29

N2 - Extreme Learning Machine has attracted widespread attention for its exemplary performance in solving regression and classification problems. It is a type of single layer feed-forward neural machine which relies on randomly allocating the input weights and hidden layer biases. Through this, the ELM has been found to possess running time spans which are within millisecond regime. It does not require complex controlling parameters which makes its implementation elementary. This paper investigates the performance of employing Extreme Learning Machine as a classifier to be used for the face recognition problem. Viola Jones algorithm has been employed to detect and extract the faces from the dataset. Finally, Histogram of Oriented Gradients (HOG) features are extracted which form the basis of classification. The scheme so presented has been tested on standard face recognition datasets from AT&T and YALE. The resulting training/testing time spans of the whole scheme range from milliseconds to seconds, dictating the compatibility of ELM with real-time events.

AB - Extreme Learning Machine has attracted widespread attention for its exemplary performance in solving regression and classification problems. It is a type of single layer feed-forward neural machine which relies on randomly allocating the input weights and hidden layer biases. Through this, the ELM has been found to possess running time spans which are within millisecond regime. It does not require complex controlling parameters which makes its implementation elementary. This paper investigates the performance of employing Extreme Learning Machine as a classifier to be used for the face recognition problem. Viola Jones algorithm has been employed to detect and extract the faces from the dataset. Finally, Histogram of Oriented Gradients (HOG) features are extracted which form the basis of classification. The scheme so presented has been tested on standard face recognition datasets from AT&T and YALE. The resulting training/testing time spans of the whole scheme range from milliseconds to seconds, dictating the compatibility of ELM with real-time events.

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A2 - Taniar, David

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Sehra K, Rajpal A, Mishra A, Chetty G. HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine. In Misra S, Gervasi O, Murgante B, Stankova E, Korkhov V, Torre C, Rocha AMAC, Taniar D, Apduhan BO, Tarantino E, editors, Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part V. Vol. 11623. Cham, Switzerland: Springer. 2019. p. 423-435. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-24308-1_35

HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine

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