Exploiting the Golden Ratio on Human Faces for Head-Pose Estimation

被引:0
|
作者
Fadda, Gianluca [1 ]
Marcialis, Gian Luca [1 ]
Roli, Fabio [1 ]
Ghiani, Luca [1 ]
机构
[1] Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a novel method for automatic head pose estimation is presented. This is based on a geometrical model of the head, in which basic features for estimating the pose consist in eyes and nose coordinates only. Worth noting, the majority of state-of-the-art approaches requires at least five features. The novelty of our work is the exploitation of the Vitruvian man's proportions and the related "Golden Ratio". The "Vitruvian man" is the well-known master-work by Leonardo Da Vinci, never used for automatic head pose estimation. Proposed method is compared by experiments with state-of-the-art ones, and shows a competitive performance although its simplicity and its low computational complexity.
引用
收藏
页码:280 / 289
页数:10
相关论文
共 50 条
  • [1] WiHead: WiFi-Based Head-Pose Estimation
    Liu, Yiming
    Konomi, Shin'ichi
    [J]. DISTRIBUTED, AMBIENT AND PERVASIVE INTERACTIONS: SMART LIVING, LEARNING, WELL-BEING AND HEALTH, ART AND CREATIVITY, PT II, 2022, 13326 : 69 - 86
  • [2] Head-Pose Estimation In-the-Wild Using a Random Forest
    Valle, Roberto
    Miguel Buenaposada, Jose
    Valdes, Antonio
    Baumela, Luis
    [J]. ARTICULATED MOTION AND DEFORMABLE OBJECTS, 2016, 9756 : 24 - 33
  • [3] Integrating perceptual level of detail with head-pose estimation and its uncertainty
    Martinez, Javier E.
    Erol, Ali
    Bebis, George
    Boyle, Richard
    Twombly, Xander
    [J]. MACHINE VISION AND APPLICATIONS, 2009, 21 (01) : 69 - 83
  • [4] Integrating perceptual level of detail with head-pose estimation and its uncertainty
    Javier E. Martinez
    Ali Erol
    George Bebis
    Richard Boyle
    Xander Twombly
    [J]. Machine Vision and Applications, 2009, 21
  • [5] Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation
    Lathuiliere, Stephane
    Juge, Remi
    Mesejo, Pablo
    Munoz-Salinas, Rafael
    Horaud, Radu
    [J]. 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, : 7149 - 7157
  • [6] Monocular head-pose estimation utilizing optical flow and tracking of stable features
    Vater, Sebastian
    Leon, Fernando Puente
    [J]. TM-TECHNISCHES MESSEN, 2017, 84 (7-8) : 525 - 534
  • [7] Exploiting Fuzzy Approximator to Head Pose Estimation
    Baradaran-Khalkhali, Maryam
    Shekofteh, S. Kazem
    Toosizadeh, Saeed
    Akbarzadeh-T, Mohammad-R.
    [J]. SPA 2010: SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS CONFERENCE PROCEEDINGS, 2010, : 68 - +
  • [8] Head pose estimation of partially occluded faces
    Wenzel, MT
    Schiffmann, WH
    [J]. 2ND CANADIAN CONFERENCE ON COMPUTER AND ROBOT VISION, PROCEEDINGS, 2005, : 353 - 360
  • [9] Attention Span Prediction Using Head-Pose Estimation With Deep Neural Networks
    Singh, Tripti
    Mohadikar, Mohan
    Gite, Shilpa
    Patil, Shruti
    Pradhan, Biswajeet
    Alamri, Abdullah
    [J]. IEEE ACCESS, 2021, 9 (09): : 142632 - 142643
  • [10] An improved SNoW based classification technique for head-pose estimation and face detection
    Gundimada, Satyanadh
    Asari, Vijayan
    [J]. 34TH APPLIED IMAGERY AND PATTERN RECOGNITION WORKSHOP: MULTI-MODAL IMAGING, 2006, : 94 - +