Head Pose Estimation

Head Pose Estimation

Dileep Reddy K

August 19, 2013

Head Pose Estimation

Introduction I

I Head pose estimation is the process of inferring theorientation of a human head from digital imagery

I In the context of computer vision, head pose estimation ismost commonly interpreted as the ability to infer theorientation of a persons head relative to the view of a camera

I The human head has three DOF in pose, which can becharacterized by pitch, roll, and yaw angles as pictured in Fig

I An ideal head pose estimator must demonstrate invariance toa variety of image-changing factors such as facial expression,lighting conditions and the presence of accessories like glassesand hats

Head Pose Estimation

Introduction II

Figure: Head image

Head Pose Estimation

Motivation I

I Physiological investigations have demonstrated that a personsprediction of gaze comes from a combination of both headpose and eye direction

I Consequently, to computationally estimate human gaze in anyconfiguration, an eye tracker should be supplemented with ahead pose estimation system

I People use the orientation of their heads to convey richinterpersonal information

I Like speech recognition, head pose estimation will likelybecome an off-the-shelf tool to bridge the gap betweenhumans and computers

I Has scope in HCI,Smart rooms & automative safety

Head Pose Estimation

Motivation II

Figure: Fig. 2. Wollaston illusion: Although the eyes are the same inboth images, the perceived gaze direction is dictated by the orientation ofthe head

Head Pose Estimation

Existing techniques for head pose estimation I

I Appearance template methods

I Detector array methods

I Nonlinear regression methods

I Manifold embedding methods

I Flexible models(EGM,AAM)

I Geometric methods

I Tracking methods

I Hybrid methods

Head Pose Estimation

Results

Figure: Results

Head Pose Estimation

Geometric methods for head pose estimation I

A. Gee and R. Cipolla, Determining the Gaze of Faces inImages, Image and Vision Computing, vol. 12, no. 10, pp.639-647, 1994.

Jian-Gang Wang and Eric Sung, ”EM enhancement of 3Dhead pose estimated by point at infinity”,Image and VisionComputing, Volume 25 Issue 12, December, 2007

Head Pose Estimation

Gee and Cipolla I

Assumptions

I Model ratios Rm = Lm/Lf ,Rn = Ln/Lf

I Weak perspective imaging process,frequently assumed invision research

I Facial features (eye ,mouth corners and nose point)are visibleand are given

Method

I ~n = [sinσ cos τ, sinσ sin τ,− cosσ]

I slant angle σ can be calculated using model ratio Rn and θ

Head Pose Estimation

Gee and Cipolla II

Figure: Facial model

Head Pose Estimation

Figure: method-1

Head Pose Estimation

Results of Gee and Cipolla

Figure: Results of Gee and Cipolla

Head Pose Estimation

Jian-Gang Wang and Eric Sung I

Assumptions

I weak perspective projection

I Two outer eye corners, two inner eye corners and two mouthcorners

I model ratio r = De/Dm

Head Pose Estimation

Jian-Gang Wang and Eric Sung II

Figure: Model face

Head Pose Estimation

Results of Jian-Gang Wang and Eric Sung

Figure: Results

Head Pose Estimation

Facial feature point extraction methods I

Motivation

I Face normalization is required to support face recognition bynormalizing a face for position so that the error due to facealignment is minimized

I Experiments performed by Turk and Pentland report ap-proximately 96% correct classification over lighting variation,while performance drops dramatically with orientation (85%)and size(64%) changes. For this rea- son face normalization isneeded.

I Berg proposed a rectification procedure using facial featurepoints

Head Pose Estimation

Classification

I Edge detection based methods

I Intensity image based techniques

I Hybrid methods

Head Pose Estimation

Exemplars

I Hough transform

I Deformable templates

I Active contours

I Integral projection