Machine Learning for Face Recognition

Machine Learning – CAB420

Table of Contents
1. Introduction................................................................................................................................2
2. Related works.............................................................................................................................3
PCA..............................................................................................................................................3
Recognition..................................................................................................................................5
Eigen values and Eigen vectors...................................................................................................7
Eigen Faces..................................................................................................................................7
3. Proposed Method.......................................................................................................................8
Face image representation:...........................................................................................................8
Mean and mean centered images:................................................................................................8
Covariance matrix:.......................................................................................................................8
Eigen face space:..........................................................................................................................9
Recognition step:........................................................................................................................10
4. Experiments..............................................................................................................................10
5. Conclusion................................................................................................................................11
References.....................................................................................................................................12
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1. Introduction
For finding the person while the user asks for accessing the resource of the particular
network the biometrics are used mainly. The main purpose of usage of biometrics is finding the
person to verifying whether the person who is accessing the network is the same person as
logged in. the biometrics may include the face structure, eye color, finger structure, etc. the
comparison will be done using the existing data of the person and the data of the person who are
accessing the network now ("A Comparative Studyon Kernel PCA and PCA Methods for Face
Recognition", 2016).
The biometrics will have many types such as face recognition, face detection, fingerprint
detection, eye recognition, eye color recognition, etc. it will be varied to human by the human in
the environment (Agarwal, 2016). This biometrics will be used in many of the systems such as
surveillance system person identification, identification in criminal acts, etc. this is not
changeable. This data cannot be lost. It is different for all peoples.
Here we need to carry out the research using the provided Caltech 256 dataset. The
provided dataset contains 256 object categories. The given dataset is too big. So that we used a
similar dataset with less amount of data. So that we can simulate that in Mat lab. For analyzing
the Caltech 256 dataset Mat lab requires a huge time bound. But the developed solution capable
of processing the Caltech 256 dataset also (Bottou, 2013).
The image recognition is well known for logos, buildings, people, objects, places and
other variables in the images. The vast amount of information is shared by the users through
social networks, apps and websites. Apart from this the mobile phones are also well equipped
these days with cameras which are becoming limitless sharing of digital videos and images.
With the help of huge data the companies are offering smart services for the people who are
using it.
Image recognition refers to as a method to recognize and identify an object or a feature in
a digital image or a video; it is also a section of computer vision. It consist of processing,
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gathering of data and analyzing the data which is extracted from the real world. The data which
is taken is a high-dimensional and also makes symbolic information or numerical form while
taking up the decisions. Excluding the image recognition the other things which are included in
computer vision are object recognition, image reconstruction, event detection and also video
tracking.
Looking it with a business perspective, big uses of image recognition are namely
recognition of face, surveillance and security, sight geo location, material recognition, action
recognition, code recognition, factory automation, picture analysis in health and chauffeur
assistance. These uses are forming growth opportunities in various areas. Let us now take a look
at how a revolution been created in a few business sectors because of image recognition.
2. Related works
PCA
Karl Pearson in 1901 invented the Principal component analysis (PCA). PCA is reduction
(variable) process and helpful in the cases when the data obtained contains some redundancy.
This will make sure that variables are reduced into smaller amount of variables which are known
Principal Components that will account for majority of the variance found in the observed
variable.
If we want to use recognition for higher dimensional space there could be a problem which can
arise. The main objective of PCA is to lower down the dimensionality of the information through
retaining some of the variables as they are in original data set. Apart from this there will be an
information loss if hand dimensionality is done. Through the best principal components the low
dimensional space can be calculated.
The benefit of PCA is that it can be used in Eigen face approach which will assist in
lowering the dimension of the database which will be used to identify in a test image. The
storage of the images is in the form of feature vectors in database and they keep moving out as
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