Structural features for face recognition

Structural Features For Face
Recognition
Prepared By: Sari Meriem
1
UNIVERSITY of MOHAMED CHERIF MESAADIA
SOUK-AHRAS
Presentation About:

Introduction
2
In this presentation we’re going to talk about features used in face
recognition and exactly geometric based features.
What are the geometric features?
Sari Meriem Structural Features 1ére Année Master GL

Introduction
3
Geometric features:
 Are features of objects constructed by a set of geometric elements like points,
lines, curves or surfaces.
 Combining machine learning and computer vision to solve visual tasks.
 Aim to find a set of representative features of a geometric form to represent an
object by collecting geometric features from images.

First Article
4
First Article:
Face Recognition: Features versus Templates
By Roberto Brunelli and Tomaso Poggio

Abstract
5
The principal idea:
 Go through different methods for human faces recognition by a machine.
 Chose two strategies tested on the same database to compare between them.
 Implementing one algorithme for each startegy:
 Based on geometric features with a recognition rate of 90%.
 Based on grey-level template matching with a perfect recognition rate.

Used DataBase
6
 Contains 188 images.
 26 males and 21 female.
 4 pictures for each person.
 Shots from frontal view.
 Environnement exposed to sunlight through windows.
 Scale variations were about 30% max.

Geometric Feature Based Matching
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The main idea is to extract relative position and other parameters of distinctive
features such as the eyes, mouth, nose and chin.

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 The overall configuration uses a numerical vector representing the position and
size of the main facial features.
 The set of features should satisfy the use:
 The min estimation possible.
 The min light conditions dependency possible.
 The min small facial expression change dependency.
 Get the max information possible.
 In this proposed algotithme there are three main steps:
 Normalization.
 Feature Extraction.
 Recognition Performance.

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 Normalization:
 It allows us to get results without taking in consederation the position, scale or
rotation.
The translation dependency can be eliminated by setting the coordinates to a
point that is detected in each image.
 In this paper that point is the eye-to-eye axis.

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 Feature Extraction:
 Face recognition offers a set of interesting constraints:
 Belatiral symetry of the face.
 Each face has two eyes, one nose and one mouth.
 It eases the task of feature extraction.
 The used extraction technique is the integral projection because it determines
the position of the features.
 Horizontal projection offers the left and right boundries of the face and the
nose.
 Vertical projection detects the head top, eyes, nose base and mouth.

11Sari Meriem Structural Features 1ére Année Master GL

12
 In total we get 35 geometrical features extracted automaticaly in this system
and used in facial recognition as next:
 Eyebrow thickness and vertical position at the eye center position.
 A coarse description of the left eyebrow’s arches.
 Nose vertical position and width.
 Mouth vertical position, width, height upper and lower lips.
 Eleven radii describing the chin shape.
 Face width and nose position.
 Face width halfway between nose tip and eyes.

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 Recognition Performance:
 We already have associated previously with each face a 35 dimention numerical
vector.
 We used Bayes classifier.
 The aim of the paper is to characterize the performance as a function of the
number of the classes to be descriminated.
 We can get useful data by estemating the intraclass and the interclass variability
Find the minimum distance to a wrong correspondence over the maximum
distance to a correct correspendence.
 In this paper we represented each class by a single element so that the
maximum distance reduces the distance from the representing vector.

Template Matching
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The simpliest version:
 Use a bidimensional array of intensity values coded image.
 Compare it with a single template representing the whole face using a metric
that is usualy euclidean distance.
 The more sophisticated versions:
 Use different viewpoints shots.
 Use a single template with qualitative prior model of how face transforms under
viewpoints change  Elastic Template Technique.

Template Matching
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 The implemented system is an extension of the work of Baron.
 We used the same normalization method cited previously.
 When finding an unknown person i.e. unclassified image, it is compared with all
of the DB images to return a vector of matching scores so that it can be classified
as the one giving the highest score.
 Used features are sorted by decreasing performance as follow:
 Eyes.
 Nose.
 Mouth.
 Whole face template.

Template Matching

Template Matching
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 Performance can be improved by using templates from more than one image
per person.
 Using templates from two images per person in the experiments has shown a
perfect recognition.

Conclusion
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 In this paper there are two developped algorithmes:
 Identification through a vector of geometrical features.
 Allow higher recognition speed.
 Smaller memory requirements(1 byte per feature).
 Identification through a template matching startegy.
 Superior recognition rate.
 Simpler.
 A successful object recognition architectures need to combine aspects of
feature-based approaches with template matching techniques.

Second Article
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Second Article:
Comparison Between Geometry-Based and Gabor-
Wavelets-Based Facial Expression Recognition Using Multi-Layer
Perceptron
Zhengyou Zhang, Michael Lyons, Michael Schuster and Shigeru Akamatsu

Abstract
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The principal idea is:
 Investigate the use of types of features in facial recognition:
 Geometric features which are a set of positions of face componenets.
 A multi-scale/oriented features called Gobor wavelet coefficients.
 The architecto developped is based on 2 layer perceptron.
 The first layer of the perceptron performs a nonlinear reduction of the dimensionality of
the feature space.
 The results set shows that gabor wavelets are more powerful than geometric features.

Introduction
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 There are a number of difficulties in FER due to the variation of facial expression,
human faces and context.
 An automatic FER system needs to detect and locate the faces, extract the facial
features classify them.
 For the face detection there lots of methods but studies showed that neural
network based ones are the most successful.
 For the feature extraction we have two approaches: geometric based and
template matching.
 In this paper facial recognition is done through elastic graph matching.

Used DataBase
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It contains 213 images of female facial expressions.
The head is almost in frontal pose.
The final distance between the eyes after rescaling is 60 pixels.
The number of images corresponding to each of the 7 categories of expression
(neutral, happiness, sadness, surprise, anger, disgust and fear) is the same.

Used DataBase

Data Set and Representation
26
 Each image is represented in two ways.
 The first uses 34 facial points which
are the geometric position.
 The second uses features extracted
with 2D Gabor transforms.

Architecture and Training
27
 Based on a two layer perceptron.
 Two sets of features (geometric positions and Gabor wavelet coefficients) are extracted.
 These features are fed in the input units of the two-layer perceptron.
 The first layer aims to perform a nonlinear reduction of the dimensionality of feature
space.
 The second layer makes a statistical decision based on the reduced set of features.
 An output unit is associated with a particular facial expression.

Experiments
28
 We use the cross-validation technique to test different configurations of our FER
architecture.
 We have carried out experiments on the FER using the developed architecture
by using geometric positions alone, using Gabor wavelet coefficients alone, and by
using the combination of the two pieces of information.

Experiments
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 The recognition rate achieved by our system is 90.1% with 7 hidden units.

Experiments After Excluding Fear Images
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 The system achieves a generalized recognition rate of:
 73.3% with geometric positions alone.
 92.2% with Gabor wavelet coefficients alone.
 92.3% with combined information.

Conclusion
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In this paper we used of types of features in facial recognition:
 Geometric features which are a set of positions of face components.
 Recognition rate of 73.3% when used alone.
 A multi-scale/oriented features called Gobor wavelet coefficients.
 Recognition rate of 92.2% when used alone.
 A recognition rate of 92.3% when combined together.

Structural features for face recognition

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