+ All Categories
Home > Documents > Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012.

Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012.

Date post: 17-Dec-2015
Category:
Upload: benedict-gilbert
View: 220 times
Download: 4 times
Share this document with a friend
22
Smile Detection by Boosting Pixel Differences Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012
Transcript
  • Slide 1
  • Caifeng Shan, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012
  • Slide 2
  • INTRODUCTION METHOD EXPERIMENTS
  • Slide 3
  • INTRODUCTION METHOD EXPERIMENTS
  • Slide 4
  • Most of the existing works have been focused on analyzing a set of prototypic emotional facial expressions Using the data collected by asking subjects to pose deliberately these expressions In this paper, we focus on smile detection in face images captured in real-world scenarios
  • Slide 5
  • Slide 6
  • INTRODUCTION METHOD EXPERIMENTS
  • Slide 7
  • BOOSTING PIXEL DIFFERENCES S. Baluja and H. A. Rowley, Boosting set identification performance,Int. J. Comput. Vis., vol. 71, no. 1, pp. 111119, 2007 Baluja introduced to use the relationship between two pixels intensities as features.
  • Slide 8
  • they used five types of pixel comparison operators (and their inverses):
  • Slide 9
  • The binary result of each comparison, which is represented numerically as 1 or 0, is used as the feature. Thus, for an image of pixels, there are or 3312000 pixel- comparison features
  • Slide 10
  • Instead of utilizing the above comparison operators, we propose to use the intensity difference between two pixels as a simple feature For an image of 24*24 pixels, there are or 331200 features extracted
  • Slide 11
  • AdaBoost ( Adaptive Boosting ) AdaBoost learns a small number of weak classifiers whose performance is just better than random guessing and boosts them iteratively into a strong classifier of higher accuracy the weak classifier consists of feature (i.e., the intensity difference),threshold, and parity indicating the direction of the inequality sign as follows:
  • Slide 12
  • Slide 13
  • Slide 14
  • INTRODUCTION METHOD EXPERIMENTS
  • Slide 15
  • Data Database : GENKI4K consists of 4000 images (2162 smile and 1828 nonsmile) In our experiments, the images were converted to grayscale the faces were normalized to reach a canonical face of 48*48 pixels
  • Slide 16
  • Data
  • Slide 17
  • Illumination Normalization Histogram equalization (HE) Single-scale retinex (SSR) Discrete cosine transform (DCT) LBP TanTriggs
  • Slide 18
  • Illumination Normalization
  • Slide 19
  • Boosting Pixel Intensity Differences Average of (left) all smile faces and (right) all nonsmile faces
  • Slide 20
  • Impact of Pose Variation
  • Slide 21
  • Slide 22
  • Thank you

Recommended