Embed Size (px)
Citation preview
FACE RECOGNITION USING NEURAL NETWORKBy: Md. Sajedul Haque RomiDept: CSEEmail.: [email protected], [email protected]
Facial Recognition Areas Determining people's identity identification methods today are
Password/PIN Token systems (such as your driver's license).
Biometric identification systems, which use pattern recognition techniques to identify people using their physiological characteristics. Fingerprints are a classic example of a biometric
Newer technologies include retina and iris recognition.
Human eyes for Facial Recognition
Human Eyes FR (cont..)
Face recognition using characteristic points of face Biometrics system for checking
identity using cameras and 3D scanners
System must to recognize picture and to do verification.
For verification Face has about 80 characteristic parameters.
Characteristic points of face
Facial Bones Anatomy
80 characteristic parameters
some of them are: width of nose, space between eyes, high of eyehole, shape of the zygomatic bone and jaw width
NN for Biometric facial Recg. Train the neural network to
recognize face from picture The NN will take some picture's
parameters for input and try to predict a person how has this characteristic.
About data set
Choice characteristic parameters: 8 characters for Data Set Input (J1-J8)
Face Extraction
A program like Abrosoft which can do face extraction in sense to find characteristic points.
Calculation
Coordinates of middle of nose,
middle points of left and right eye, mouth, middle between eyes and points of ends of nose width.
Calculation (cont..)
Coordinates of middle of nose,
middle points of left and right eye, mouth, middle between eyes and points of ends of nose width.
Calculate the Distance of two points (x1, y1) and (x2, y2)
Calculation(normalization)
X - value that should be normalizedXn - normalized valueXmin - minimum value of XXmax - maximum value of X
Training Neural Network
Training Neural Network for Face Recognition with Neuroph Studio
Training Neural Network To teach the neural network we need training data
set. The training data set consists of input signals
assigned The neural network is trained using supervised learning algorithms
It uses the data to adjust the network's weights and thresholds to minimize the error on the training set.
If the network is properly trained, it has then learned to model the (unknown) function that relates the input variables to the output variables, and can subsequently be used to make predictions where the output is not known.
Training Neural Network ( 5steps)The 5 Steps
Conslusion
we created one basic training set . We normalize the original data set
using a linear scaling method. Through 5 basic steps we explained in detail the creation, training and testing neural networks.
We have shown that the best solution to the problem of face recognition using Neuroph
Back To The Pavilion
Disguised Face
Disguised Face Recognition
Altered Face recognition Algorithms (1) PCA algorithm with Mahalanobis
distance (Alexander & Smith, 2005) (2) Half-face based algorithm
(Ramanathan et al., 2004, (3) Eigen-eyes based algorithm
(Silva & Rosa, 2003)
Thank You!
Thanks to all! Md. Sajedul Haque Romi Dept: CSE email.: [email protected],
[email protected] Facebook: fb.com/JonabROMI
