Transcript Attribute and Smilie Classifiers for Face Verification

Week 7 Report
Misrak Seifu, Wesna Lalanne
Mentor: Mahdi M. Kalayeh
Overview of week 7
 Annotating
25K images
 Feature extraction



Hog
Lbp
Gabor
 Beginning
the face alignment
 Training attribute detectors
 Testing detectors on a subset of data
Feature Extractions
 Hog
 Lbp
 Gabor
Local features
Holistic feature
Chehra Face Alignment
 Detects
66 facial landmark points
 Uses ipar-CLR method to continuously update the
generic model.
 Ipar-CLR: incrementally adding new training
samples and updating the cascade of regression
functions.
Train attribute Detector
 Features
extracted only from faces
 No alignment were used
 Binary attribute detector were trained
 ~4K training images
Results of attribute detectors
 Tested
on a subset of data (~8K images)
Attribute
Accuracy
Female
86.61%
Male
78.20%
Teenager
14.74%
Youth
73.86%
Middle age
66.67%
White
61.18%
Black
77.27%
Asian
39.37%
Other
15.38%
Oval
64.67%
Round
37.88%
Heart
16.24%
Smiling
62.89%
Mouth open
41.18%
Eyeglasses on
68.93%
Sunglasses on
87.71%
Lipstick on
32.22%
Duck face
36.19%
Phone shown
47.80%
Using mirror
48.41%
Plan for week 8
 Aligning
detected faces
 Feature extraction from aligned faces
 Feature extraction from non-face parts of image
 Extracting local features from facial landmarks
 Train attribute detector