This document discusses Shweta Somani's enrollment number and a face recognition project. The project focuses on developing a fast, simple and accurate computational face recognition model that works in different environments. Two algorithms were applied: Eigenfaces and Local Binary Patterns. Eigenfaces does not handle external variations well and requires more training images, while Local Binary Patterns handles external variations and requires fewer training images.

1. Shweta Somani
Enroll no. - 10101080

2. Face recognition is one of the major
challenge and is the most popular research
areas in the computer vision. It has various
potential applications, such as -
 person identification.
 human-computer interaction.
 security systems.

3.  Given a training database of facial images
annotated with identity.
 Train a system to recognize the identity of a
person from a new image of the person.
 The system should have minimal sensitivity
to lighting, maximal probability of detecting
the correct person, and minimal probability of
giving false response.

4. This project is focused to develop the
computational model of face recognition that
is fast, simple and accurate in different
environments. I have applied 2 algorithms
that are
 Eigen faces algorithm
 Local Binary Pattern algorithm

5.  Face and eye detection from Haar and LBP
cascade classifiers
 Extraction of features from Principal
component Analysis
 Training through eigenfaces and LBP
algorithm
 Recognition
 Validation by seeing the % of accuraracy.

6. Eigen faces
1)Does not show proper result when
variation is generated by external sources.
2)Data is treated as high dimensionality
vector.
3)It requires training of atleast 40 images for
proper result.
Local Binary Pattern
1)Show proper result when variation is
generated by external sources.
2)Data is treated as low dimensionality
vector.
3)It requires training of only 4 to 5 images
for proper result.