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2021 | OriginalPaper | Chapter

GAPER: Gender, Age, Pose and Emotion Recognition Using Deep Neural Networks

Authors : Deepali Virmani, Tanu Sharma, Muskan Garg

Published in: Advances in Electromechanical Technologies

Publisher: Springer Singapore

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Abstract

Facial emotion recognition is identifying human emotion from facial expressions. As the concept is gaining wide popularity with time, researchers are trying to expand the horizon by providing efficient techniques to extract more knowledge from facial features. Hence, this paper presents a multiple face detection system, which is capable of analyzing human facial features for predicting human emotion, pose, gender and age. To the best of our knowledge, no single paper presents a system capable of detecting all these features together in a single frame. Haar cascades and deep convolutional neural networks form the backbone of the entire work. A support of a Web application using flask is also provided, which will enable one to implement the work on another computer, in the same network, via a Web browser. The experimental results of the system provide an accuracy of 68.33%.

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Literature
1.
Emerich S, Lupu E, Apatean A (2009) Emotions recognition by speech and facial expressions analysis. In: 17th European signal processing conference (EUSIPCO 2009)
2.
Huang C, Pan W, Lin S (2009) Gender recognition with face images based on PARCONE model. In: Proceedings of the second symposium international computer science and computational technology (ISCSCT), pp 222–226
3.
Starovoitov VV, Samal DI, Briliuk DV (2002) Three approaches for face recognition. In: The 6-th international conference on pattern recognition and image analysis, Velikiy Novgorod, Russia, pp 707–711, 21–26 Oct 2002
4.
Terzopoulos D, Waters K (1993) Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Trans Pattern Anal Machine Intell 15(6):569–579
5.
Grossard C, Chaby L, Hun S, Pellerin H, Bourgeois J, Dapogny A, Ding H, Serret S, Foulon P, Chetouani M, Chen L, Bailly K, Grynszpan O, Cohen D, Children facial expression production: influence of age, gender, emotion subtype, elicitation condition and culture
6.
Lench HC, Flores SA, Bench SW (2011) Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: a meta-analysis of experimental emotion elicitations. Psychol Bull 137(5):834–855
7.
8.
O’Regan K (2003) Emotions and E-learning. JALN 7(3):78–92
9.
Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. Computer Vision Foundation
10.
Kwon YH, Da Vitoria Lobo N (1993) Locating facial features for age classification. In: Proceedings of SPIE-the international society for optical engineering conference, pp 62–72
11.
Mélange PA, Sable GS (2018) Age group estimation and gender recognition using face features. Int J Eng Sci (IJES) 7(7 Ver. I):01–07. ISSN (e): 2319 – 1813 ISSN (p): 23-19 – 1805
12.
Tin HHK (2011) Gender and age estimation based on facial images. ACTA TECHNICA NAPOCENSIS; Electron Telecommun 52(3)
13.
Guo G, Mu G (2014) A framework for joint estimation of age, gender and ethnicity on a large database. Image Vis Comput 32(10):761–770
14.
Ranjan R, Sankaranarayanan S, Castillo CD, Chellappa R (2017) An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE international conference on automatic face & gesture recognition
15.
Dehghan A, Ortiz EG, Shu G, Masood SZ (2017) DAGER: deep age, gender and emotion recognition using convolutional neural network. arXiv:​1702.​04280v2 [cs.CV], 4 Mar 2017
16.
Mansur ZAE, Mohammed Esmaail NF, Mansur FAE, Review on face recognition using fuzzy logic hidden markov and neural network Techniques. Int J Sci Res (IJSR)
17.
Haar A (1910) For orthogonal function systems theory. German Math Res J
18.
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Accepted conference on computer vision and pattern recognition
19.
Jayapradha J, Sharma S, Dugar Y (2018) Detection and recognition of human emotion using neural network. Int J Appl Eng Res 13(8):6472–6477. © Research India Publications. ISSN 0973-4562
20.
Arel I, Rose DC, Karnowski TP (2010) Deep machine learning—a new frontier in artificial intelligence research [research frontier]. IEEE Comput Intell Mag 5(4):13–18
21.
Paliwal M, Kumar UA (2009) Neural networks and statistical techniques: a review of applications. Expert Syst Appl 36(1):2–17CrossRef
22.
Metadata
Title
GAPER: Gender, Age, Pose and Emotion Recognition Using Deep Neural Networks
Authors
Deepali Virmani
Tanu Sharma
Muskan Garg
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Electromechanical Technologies

Print ISBN: 978-981-15-5462-9

Electronic ISBN: 978-981-15-5463-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-5463-6_26

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