Face Recognition Based on Features Measurement Technique

被引:1
|
作者
Fakhir, M. M. [1 ]
Woo, W. L. [1 ]
Dlay, S. S. [1 ]
机构
[1] Newcastle Univ, Sch Elect & Elect Engn, Newcastle Upon Tyne, Tyne & Wear, England
关键词
face recognition; measurement; dimensions; geometrical shape; calibration;
D O I
10.1109/EMS.2014.54
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
we present a new technique to infer dimensions that can be used in biometric face recognition. The methodology is centered on inferring unique dimensions from human ears which provides unique physical biometric features. The process of determining the distance is done by harvesting the real actual dimensions from 2D faces images. This is achieved by using specific point to point distances on the two ears in human face. The points chosen give dimension information which enables discrimination for face recognition. The empirical results confirm that an accuracy of 94% recognition rate is achievable. The different positions of measurement points on the ears have a powerful impact to reduce the error of face recognition. Hence, our new measurement dimensions technique is precise and nodal facial points can be reflected as a robust face recognition method.
引用
收藏
页码:158 / 162
页数:5
相关论文
共 50 条
  • [21] Multi-Features Fusion Based Face Recognition
    Long, Xianzhong
    Chen, Songcan
    [J]. NEURAL INFORMATION PROCESSING (ICONIP 2017), PT VI, 2017, 10639 : 540 - 549
  • [22] Improving Face Recognition Methods based on POEM Features
    Lenc, Ladislav
    Kral, Pavel
    [J]. ICAART: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2, 2020, : 538 - 545
  • [23] FACE RECOGNITION BASED ON SIGMA SETS OF IMAGE FEATURES
    Srinivasan, Ramya
    Nagar, Abhishek
    Tewari, Anshuman
    Mitrani, Donato
    Roy-Chowdhury, Amit
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [24] Face recognition algorithms based on transformed shape features
    Biswas, Sambhunath
    Biswas, Amrita
    [J]. International Journal of Computer Science Issues, 2012, 9 (3 3-3): : 445 - 451
  • [25] A new approach to face recognition based on features fusion
    Fu, Yanhong
    [J]. PROCEEDINGS OF THE 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER ENGINEERING AND ELECTRONICS (ICECEE 2015), 2015, 24 : 1398 - 1404
  • [26] ICA-BASED FEATURES FUSION FOR FACE RECOGNITION
    Wei, Xiaopeng
    Zhou, Changjun
    Zhang, Qiang
    [J]. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2010, 6 (10): : 4651 - 4661
  • [27] Face recognition based on statistical features and SVM classifier
    Ben Chaabane, Slim
    Hijji, Mohammad
    Harrabi, Rafika
    Seddik, Hassene
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (06) : 8767 - 8784
  • [28] Face recognition based on statistical features and SVM classifier
    Slim Ben Chaabane
    Mohammad Hijji
    Rafika Harrabi
    Hassene Seddik
    [J]. Multimedia Tools and Applications, 2022, 81 : 8767 - 8784
  • [29] A frequency domain face recognition technique based on correlation plane features as input to a regression neural network
    Banerjee, Pradipta K.
    Cahndra, Jayanta K.
    Datta, Asit K.
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE AND EXHIBITION ON BIOMETRICS TECHNOLOGY, 2010, 2 : 75 - 82
  • [30] Massive Face Recognition Algorithm Based on the Hadoop Technique
    Chen, Wen-Yuan
    Li, Ming-Ming
    Lin, Jian-Shie
    Ni, Sheng-Chung
    [J]. 2016 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C), 2016, : 789 - 792