Face recognition is a technique for identifying or verifying individuals using their facial features. It involves extracting features from images, selecting subsets of important features, and matching feature sets to identify faces. 2D recognition uses image features while 3D recognition uses geometric facial features, improving accuracy over 2D. Face recognition has applications in security, biometrics, law enforcement, and advertising. Key algorithms include PCA, LDA, neural networks, and recently deep learning methods.

1. -DAKSH VERMA

3.  “From a database of faces, identify or verify one or
more persons in a given still or video image.”
 A vital option for granting access to an individual in
various physical and virtual domains.
 Applications in fields like video surveillance, national
id, passport, windows login
 Popular area of research for the past decade.

4. 2D and 3D Face Recognition
 In 2D Recognition, features are extracted from the
image and used to search images with similar features.
 In 3D Recognition, the three-dimensional geometry of
the human face is used.
 3D face recognition methods can achieve higher
accuracy than their 2D counterparts.
 3D techniques do not suffer from illumination, facial
expressions, pose changes as compared to 2D
techniques.

5. Process of Face Recognition
Feature
Extraction
• Extracting face features like nose, eyes.
• Transforming it into a set/vector.
Feature
Selection
• Selecting a subset of features from the
extracted features
Face
Recognition
• The data sets containing features are
matched to the image database

6. Metrics
 False Acceptance Rate: It is the probability that the
system incorrectly authorizes a non-authorized
person, due to incorrectly matching the biometric
input with a template.
 False Rejection Rate: It is the probability that the
system incorrectly rejects access to an authorized
person, due to failing to match the biometric input
with a template.
 A system with both low FAR and FRR is considered
said to have good discriminating power.

7. Advantages
 Convenient, Social Adaptability
 More User Friendly
 Inexpensive Technique of Identification
 Higher Success Rates
 Non-Intrusive

8. Problems
1. Pose Variations: Ideally Frontal images, Large pose
variations lead to degradation of results
2. Occlusions: Beard, Hat, Partially Covered Faces
3. Age: Change in muscle tension
4. Facial Expressions: Can run in thousands and hence
provide a challenge
5. Illumination: Can accentuate or diminish certain
features of the face

9. Applications
Law
Enforcement
• Video Surveillance
• Forensic
Reconstruction of
Faces from
Remains
Biometrics
• Person
Identification
• Automated
Identity
Verification
Information
Security
• Access Security
• Data Privacy
• User
Authentication

11. Principal Component Analysis
 Performs a dimensionality reduction by extracting the
principal components from the multi-dimensional
data.
 Finds the uncorrelated variables called principal
components from a set of correlated data variables.
 PCA to reduce the dimensionality of the feature space
by calculating the eigenvectors of the covariance
matrix of the set of N2-dimensional feature vectors,
and then projecting each feature vector onto the
largest eigenvectors.

12. Linear Discriminant Analysis
 A better alternative to the PCA
 Provides discrimination among the classes
 The main aim of LDA is to find a base of vectors which
provides the best discrimination among the classes,
maximize the between-class differences, and minimize
the within-class differences
 The difference between LDA and PCA is that LDA
explicitly attempts to model the difference between
the classes of data whereas PCA does not take into
account any difference in class.

13. EigenFaces/EigenPictures
 An NxN image I is linearized in a N2 vector, so that it
represents a point in a N2-dimensional space.
 Comparisons are performed on a low dimensional
space found by dimensionality technique like PCA.
 The images in the database are represented as vector of
weights.
 The test image is represented as a vector of weights,
and the closest weight (in Euclidean distance) image is
obtained from the database

14. Neural Networks
 Most popular algorithm: Back Propagation Neural
Network (BPNN)
 It is used in multilayer perceptron (MLP)
 MLP is a feed-forward (no directed cycle) network of
nodes in a directed graph which are organized in
multiple layers.
 To reduce the amount of input data, PCA is used for
feature extraction and BPNN for pattern recognition.

15. Neural Networks
Pre Processed
Input Image
Principal
Component
Analysis
(PCA)
Back
Propagation
Neural
Network
(BPNN)
Classified
Output
Image
• Can be used to address problems like face
recognition, gender classification, and facial
expressions classification

16. Infrared Images
 Insensitive to illumination
 Alternate source of information for detection and
recognition
 Drawbacks:
 Sensitive to changes in temperature
Lower resolution of Infrared images
 The radiated level of heat from the face can be used to
find the stress levels in human and hence can be used
for lie detection.

17. Infrared Images Process
1. Capture thermogram human face from infrared
camera
2. Preprocess the captured thermogram
3. Extract the features like temperature distribution
statistics, the perimeter of the face contour.
4. Apply BPNN for recognition

19. 3D Assisted Face Recognition: Dealing
With Expression Variations
 Expressions- critical source of variation in face
recognition.
 An animatable 3D model is generated for each user
based on 17 automatically located landmark points
 This produces synthetic face images with different
expressions
 Apply recognition techniques like PCA and LDA

20. DeepFace
 Uses a 3D model for rotating faces virtually so that the
person in the photo appears to be looking at the
camera.
 Derive a face representation from a nine-layer deep
neural network containing more than 120 million
parameters (nodes)
 Accuracy of 97.35% on the Labeled Faces in the Wild
(LFW) dataset, as compared to the human accuracy of
97.53%

21. Face Recognition after Plastic
Surgery
 Plastic Surgery - alter the original facial information to
a great extent
 Process is based on the assumption that the inner
consistency of the face after plastic surgery is
preserved
 The face is divided into two patches and designing a
classifier for each patch using Gabor feature and Fisher
LDA
 Fuse together the rank order list of each classifier

23. • Use gender classification, to show ads
according to gender
• Capture user’ facial expressions to know
likeness of an ad, brand
Advertising
• Cameras at retail store to take pictures and
identify customers
• Deduct the bill from associated customer
account
Retailing
• 3D Face Recognition is still very fresh
research area
• Combination of various feature extraction
and selection techniques can be studied
Research