CNN BASED DETECTION OF EMOTION,AGE,GENDER

Article Sidebar

PDF
Published: May 25, 2020
DOI: https://doi.org/10.26483/ijarcs.v11i0.6562
Keywords:
commercial bots etc

Main Article Content

Vishnuvardhan Reddy
Ravi teja Bandi

Abstract

Face is one of the most dominant points in our body. We can get many statistics like age, gender, and many others by means of inspecting the face of a human. In today’s world, laptop imaginative and prescient is been used to teach machines to know and recognize the actual world. Using numerous digital pix from webcam, videos, cameras and with the assist of deep learning, computer systems can efficiently determine out   and classify objects and then reply to what they “see†in actual world. There are a number of uses of figuring out age and gender from face like forensic testing, limiting get entry to of alcohol from wending computing device and person content material for younger people. Emotion from a face can be used to predict human laptop interaction, students/teacher’s activity in class, commercial bots etc

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 11 (2020): VOLUME 11 SPECIAL ISSUE 1, MAY 2020
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

Bogdan Kwolek, “Face Detection Using Convolutional Neural Networks and Gabor Filtersâ€, Conference Paper- September 2005, Pg.No. - 1 to 5

Imane Lasri, Anouar Riad Solh & Mourad El Belkacemi, “Facial Emotion Recognition of Students using Convolutional Neural Networkâ€, IEEE-2019

Levi, G, & Hassncer, T, "Age and gender classification using convolutional neural networksâ€, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). doi:10.1109/cvprw.2015.7301352, Pg no.-34 to 42

Rajesh Kumar G A, Ravi Kant Kumar & Goutam Sanyal, “Facial Emotion Analysis using Deep Convolutional Neural Networkâ€, 2017 International Conference on Signal Processing and Communication (ICSPC). doi:10.1109/cspc.2017.8305872, Pg.No.-369 to 374

2018-Sepidehsadat Hosseini, Seok Hee Lee, Hyuk Jin Kwon, Hyung Il Koo &Nam Ik Cho, “Age and Gender Classiï¬cation Using Wide Convolutional Neural Network and Gabor Filterâ€, IEEE-2018

Ari Ekmekjiâ€, Convolutional Neural Networks for Age and Gender Classiï¬cationâ€, Stanford University-2016