This project is about biometrics

1. GUIDE : Mrs NEHA GUPTA
GGMrsguiEHA GUPTA
Team members
GUIDE : Mrs NEHA GUPTAGUIDE : Mrs NEHA GUPTA
Team members
Team members

2. A biometric is a physiological or behavioral characteristic of a human
being that can distinguish one person from another and that theoretically
can be used for identification or verification of identity.”
Biometric applications available today are categorized into 2 sectors
- Psychological: Iris, Fingerprints, Hand, Retinal and Face recognition
- Behavioral: Voice, Typing pattern, Signature

3. Facial recognition is a form of computer vision that uses faces to
attempt to identify a person or verify a person’s claimed identity.
For face recognition there are two types of comparisons, cont…
1) IDENTFICATION - figure out “Who is X?” - accomplished by
system performing a “one-to-many ” search
2) VERIFICATION - answer the question “Is this X?” - accomplished
by the system performing a “one-to-one” search.

4. Face Recognition Face recognition systems (FRSs) are an
important field in computer vision, because it represent a
non-invasive BI technique.
1. A face detection algorithm is used for extracting faces
from video frames (training videos) and generating a face
database.
2. Filtering and preprocessing are applied to face images
obtained in the previous step.
3. A collection of machine learning algorithms are trained
using as input data the faces obtained in the previous step.
4. Finally, the classifiers are used for classify faces obtained
from video frames

5. Describe the different methods of face recognition-
Feature extraction methods- The input image to identify and
extract (and measure) distinctive facial features such as the
eyes, mouth, nose, etc. Compute the geometric relationships
among those facial points, thus reducing the input facial
image to a vector of geometric features.
Holistic methods- Holistic approaches attempt to identify
faces using global representations, i.e., descriptions based
on the entire image rather than on local features of the face.

6. During the past decades, several ML algorithms have been proposed for classification tasks.
Most of them are from the theoretical view under some assumption about data
distribution, characteristics of the classification task, signal to-noise-ratio, etc. In reality,
these assumptions are often hard to be verified. Therefore, a practical solution for selecting
an appropriate model for a given classification task is to experimentally compare these
algorithms. Five widely used machine classifiers-
K-Nearest Neighbor (KNN)
Locally-Weighted Learning (LWL)
Naive Bayes classifier (NB)
Decision Table Classifier (DT)
Single Decision Tree (SDT).

7. 1.Replacement of PIN
2. Border control 3. Voter verification
4. Computer security
5. Government Use
6. Security/Counterterrorism.
7. Immigration
8. Commercial Use
9. Residential Security
10. Banking using ATM

8. 1.E. Garc´ ıa Amaro, M.A. Nu ˜ no-Maganda and M. Morales-Sandoval, “Evaluation of
Machine Learning Techniques for Face Detection and Recognition”, IEEE 2012.
2. Claudia Iancu, Peter Corcoran and Gabriel Costache,” A Review of Face Recognition
Techniques for In-Camera Applications”, IEEE 2007.
3. Brian C. Becker, Enrique G.Ortiz, “Evaluation of Face Recognition Techniques for
Application to Facebook ” 2008 IEEE
4. D. Bhattacharyya, R. Ranjan, F. Alisherov, and M. Choi, “Biometric authentication: A
review,” International Journal of u- and e- Service, Science and Technology, vol. 3, no.
2, pp. 23– 27, 2009.
5. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and
Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006
6. G. Bradski and A. Kaehler, Learning OpenCV. O’Reilly Media Inc., 2008.