research-article

3D Face Recognition using Kinect

Authors:

Phalguni GuptaAuthors Info & Claims

ICVGIP '14: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

Article No.: 76, Pages 1 - 8

https://doi.org/10.1145/2683483.2683559

Published: 14 December 2014 Publication History

Abstract

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.

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Cited By

Faizabadi AMohammed Shweesh OMohd Zaki HMohammed AKhan MAliman N(2023)Exploring Robust Pose Invariant Face Recognition with Vision Transformers: A Multi-Modal Study2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS)10.1109/ICETAS59148.2023.10346325(1-7)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICETAS59148.2023.10346325
Oyelade OEzugwu A(2020)A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital MammographyIEEE Access10.1109/ACCESS.2020.30162238(148644-148676)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3016223
Mohammadi SGervei O(2019)Three Dimensional Posed Face Recognition with an Improved Iterative Closest Point Method3D Research10.1007/s13319-019-0232-010:3-4(1-17)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s13319-019-0232-0
Show More Cited By

Index Terms

3D Face Recognition using Kinect
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Image and video acquisition
        3D imaging
  2. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Other conferences

ICVGIP '14: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

December 2014

692 pages

ISBN:9781450330619

DOI:10.1145/2683483

General Chairs:
A. G. Ramakrishnan
IISc, Bangalore
,
Jitendra Malik
University California, Berkeley
,
Program Chairs:
Alex Efros
UC-Berkeley
,
C. V. Jawahar
IIIT Hyderabad
,
Manik Varma
Microsoft Research

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2014

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ICVGIP '14

ICVGIP '14: Indian Conference on Computer Vision Graphics and Image Processing

December 14 - 18, 2014

Bangalore, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Faizabadi AMohammed Shweesh OMohd Zaki HMohammed AKhan MAliman N(2023)Exploring Robust Pose Invariant Face Recognition with Vision Transformers: A Multi-Modal Study2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS)10.1109/ICETAS59148.2023.10346325(1-7)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICETAS59148.2023.10346325
Oyelade OEzugwu A(2020)A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital MammographyIEEE Access10.1109/ACCESS.2020.30162238(148644-148676)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3016223
Mohammadi SGervei O(2019)Three Dimensional Posed Face Recognition with an Improved Iterative Closest Point Method3D Research10.1007/s13319-019-0232-010:3-4(1-17)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s13319-019-0232-0
Azzakhnini SBallihi LAboutajdine D(2018)Combining Facial Parts For Learning Gender, Ethnicity, and Emotional State Based on RGB-D InformationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/315212514:1s(1-14)Online publication date: 6-Mar-2018
https://dl.acm.org/doi/10.1145/3152125
(2018)Face recognition from unconstrained three-dimensional face images using multitask sparse representationJournal of Electronic Imaging10.1117/1.JEI.27.1.01300827:01(1)Online publication date: 19-Jan-2018
https://doi.org/10.1117/1.JEI.27.1.013008
Mukai NZhang YChang Y(2018)Pet Face Detection2018 Nicograph International (NicoInt)10.1109/NICOINT.2018.00018(52-57)Online publication date: Jun-2018
https://doi.org/10.1109/NICOINT.2018.00018
Tang XZhao JLi WFeng B(2018)Design and Implementation of Smart Home Cloud System Based on KinectIntelligent Information Processing IX10.1007/978-3-030-00828-4_13(120-126)Online publication date: 26-Sep-2018
https://doi.org/10.1007/978-3-030-00828-4_13
Zheng CZhang CLi XZhang FLi BTang CYao CZhang TYing FGajos KMankoff JHarrison C(2017)KinToonAdjunct Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology10.1145/3131785.3131813(201-203)Online publication date: 20-Oct-2017
https://dl.acm.org/doi/10.1145/3131785.3131813
Saleh YEdirisinghe E(2017)3D face reconstruction and recognition using the overfeat network2017 8th International Conference on Information and Communication Systems (ICICS)10.1109/IACS.2017.7921956(116-119)Online publication date: Apr-2017
https://doi.org/10.1109/IACS.2017.7921956
Gaonkar AGad MVetrekar NTilve VGad R(2017)Experimental Evaluation of 3D Kinect Face DatabaseComputer Vision, Graphics, and Image Processing10.1007/978-3-319-68124-5_2(15-26)Online publication date: 21-Oct-2017
https://doi.org/10.1007/978-3-319-68124-5_2
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