This document summarizes a presentation on using histogram of oriented gradients (HOG) for face detection in machine learning image processing. It discusses preprocessing images, extracting HOG features, training a support vector machine classifier on 80% of images and testing it on 20%. Results show the classifier can correctly and incorrectly match faces. HOG features capture edge orientations to describe shapes and SVM classification maps examples to space divided by a gap to predict categories.

1. Department of Computer Science & Engineering
Sikkim Manipal Institute of Technology
A PRESENTATION ON
MACHINE LEARNING IN IMAGE PROCESSING FOR FACE DETECTION USING
HISTOGRAM OF ORIENTED GRADIENTS
BY
VISHAL SRIVASTAVA
CSE 7TH SEM. SEC ‘A’
REG. NO : 20130226
UNDER THE GUIDANCE OF:
PROF. RAJIB GHOSH
ASSISNTANT PROFESSOR
NATIONAL INSTITUTE OF TECHNOLOGY PATNA
DONE AS A PART OF SUMMER INTERNSHIP PROGRAM AT NIT PATNA

2. ABSTRACT
• With the advent of devices that penetrate every aspect of our daily lives, it’s
very important to ensure maximum security when it comes to authentication
of devices as well as for identity purposes. Machine Learning in Facial
Recognition Systems is a very important aspect for identifying individuals as
well as in identity and authentication purposes. It has vast applications in
surveillance systems, mobile authentication and individual identity revelation
and confirmation.

3. INTRODUCTION
• Face Recognition is a part of object and pattern recognition where objects are classified
using a particular feature and a model is prepared for the same. Depending on the feature
being used for the model, templates for that particular object are created which act as a
standard in isolating the object under considering from a given image.
• In this project, we shall deal with HOG features, i.e., Histogram of Oriented Gradients
features which is discussed latter in details.
• The project can be sub-categorized into the following parts:
• Selecting the feature to be extracted
• Collecting image samples & preprocessing them
• Extract the features required
• Train the model
• Match components with query image
• Display Closest match

4. OVERVIEW

5. LITERATURE SURVEY
• Bin Lia, Guang Huo recently illustrated the mechanism of face detection and recognition using
Locality Sensitive Histogram of Oriented Gradients (LSHOG). They proposed that the traditional
histograms of oriented gradients (HOG) divide an image into many cells, and computed a histogram
of gradient orientations over each cell.
• First, we use LSHOG to extract feature vectors of face images. Then, to show how different
dimension reduction algorithms affect recognition accuracy, we choose several typical dimension
reduction algorithms to reduce the dimensions of feature vectors of face images. They evaluated
LSHOG and our proposed face recognition method on four benchmark face databases.
Experimental results verify the feasibility and effectiveness of LSHOG and our face recognition
method.

6. DATASET
• The database consists of 40 different individuals where there are 10 samples of non-standard RGB
images per person. The individuals are marked from 1 to 40 and their samples are marked 1 to 10.
These images are then further subjected to preprocessing softwares and algorithms to convert
them into standard images.
TRAINING AND TEST DATA SET
• The most important part for preparing a learning model is training the classifier.
• The database is partitioned into 2 parts as:
• Training Set (80 %)
• Testing Set (20 %)
• This means that 80 % of the data (images) of every individual are used for preparing the training
data-set and rest 20 % is used for testing the Algorithm.

7. PRE-PROCESSING OF DATA

8. FEATURE EXTRACTION AND TRAINING
EXTRACTING HOG FEATURES
The Computer Vision API for MATLAB provides for
the necessary tools the extract the HOG Features
for a given Image sample. Following is the Code
with the detailed explanation for the same.
extractHOGFeatures
• Extract histogram of oriented gradients (HOG)
features
• o Syntax
• features = extractHOGFeatures(I)
• [features,validPoints] =
extractHOGFeatures(I,points)
• [___, visualization] =
extractHOGFeatures(I,___)
• [___] = extractHOGFeatures(___,Name,Value)

9. TRAINING & PARTITIONING
• The most important part for preparing a learning model is training the classifier.
• The database is partitioned into 2 parts as:
• Training Set (80 %)
• Testing Set (20 %)
• This means that 80 % of the data (images) of every individual are used for preparing the training
data-set and rest 20 % is used for testing the Algorithm.
• The following code illustrates the portioning of the database:
• % Splitting database in Training Sets & Test Sets
• [training,test]=partition(facedatabase,[0.8 0.2]);

10. CLASSIFICATION
• The choice of model is a very sensitive topic as it determines the accuracy of the algorithm which
we are implementing.
• In this case we are using ‘fitcecoc’ classifier which is the Fit multiclass models for support vector
machines or other classifiers: SYNTAX:
faceClassifier=fitcecoc(trainingFeatures,trainingLabel);
FEATURE VECTOR
The result of the above code is a 2-D
Matrix , i.e., The training feature vector
which looks like as shown below:

11. RECOGNITION
• Once the model is prepared and ready the testing phase is carried out. This is done using the Test
Data-Set that we had earlier partitioned during initial stages. Several Test samples are taken as
query images whose HOG features are extracted and then it is looped through to find the best
matched class for the given input.
• The sample results are illustrated:
CASE 1: CORRECT MATCH

12. CASE 2: INCORRECT MATCH

13. RESULTS AND ANALYSIS USING SVM
• SUPPORT VECTOR MACHINE
• Support vector machines (SVMs) are supervised learning models with associated learning
algorithms that analyze data used for classification and regression analysis. Given a set of
training examples, each marked for belonging to one of two categories, an SVM training
algorithm builds a model that assigns new examples into one category or the other, making it a
non-probabilistic binary linear classifier. An SVM model is a representation of the examples as
points in space, mapped so that the examples of the separate categories are divided by a clear
gap that is as wide as possible. New examples are then mapped into that same space and
predicted to belong to a category based on which side of the gap they fall on.
• USING libsvm (SVM Library)
• The predictive analysis is carried out using libsvm. The training & test feature vectors are
converted into suitable format before carrying out further processing using code. We have 400
image samples, with each image having a feature vector of size 4680, analysis was carried out
and results are shown later below.
• Proper scaling of training feature vector gives us more accurate results.
• The Analysis is done in 3 steps:
• Scaling
• Training
• Prediction

14. SCALING
• Syntax:
• svm-scale –l <lower bound> -u <upper bound> -s abc TrainSet.dat > ScaledSet
• Fig: 8.1 Scaling the Trained & Test Data Set

15. TRAINING
• Syntax: svm-train TrainedData.dat
• Fig:

16. PREDICTION AND ACCURACY:
• Syntax: svm-predict test_data.dat trained_data.model a.out
• Fig:

17. REFERENCES
• Face recognition using locality sensitive histograms of orientedgradientsBin Lia,∗, Guang
HuobaSchool of Information Engineering, Northeast Dianli University.
• I.T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 1989.
• H. Yu, J. Yang, A direct LDA algorithm for high dimensional data with applicationto face
recognition, Pattern Recognit. 34 (2001) 2067–2070.
• Face Recognition Algorithms, Proyecto Fin de Carrera, June 16, Ion Marqu´es,
• http://www.intechopen.com/books/face-recognition/feature-extraction-and-representation-for-
face-recognition
• http:// Wikipedia.org/wiki/Support-vector-machine
• http://scholastictutors.webs.com

18. THANK YOU