Shri Siddhi Vinayak Institute Of Technology

Face Detection & RecognitionPresented ByMohd ShakirKamender Singh GangwarPrakher AwasthiKusum Lata

Contents

Face Detection Face Detection 2 Face Recognition Face Recognition 2 Face detection & Recognition problems

Face detection problem structureFeature extraction processes.What is recognition?

Face recognition processingFace Recognition from videoClassification The Basic Idea Experimentation and Results System Overview

Main

FACE DETECTION

FACE DETECTION 2

FACE RECOGNIZE

FACE RECOGNIZATION 2

Face detection v3b*Face detection & recognition

Face detectionFace recognition

Face detectionFace recognitionMr.chang

Prof..ChengFace databaseOutput:

Face detection v3b

CSE 576, Spring 2008Face Recognition and Detection*Face detection & Recognition problemsIdentity recognitionWhere is it?Object detection

Face Recognition and Detection

The input of a face recognition system is always an image or video stream.The output is an identification or verification of the subject or subjects thatappear in the image or video. Some approaches define a face recognitionsystem as a three step process -A generic face recognition system

Face detection problem structure

Feature extraction processes.

What is recognition?Where is this particular object?

What kind of object(s) is(are) present?

Face Recognition by Humans Performed routinely and effortlessly by humans Enormous interest in automatic processing of digital images and videos due to wide availability of powerful and low-cost desktop embedded computing Applications: biometric authentication,surveillance, human-computer interactionmultimedia management

Face recognition processingA face is a three-dimensional object subject to varying illumination, pose, expression is to be identified based on its two-dimensional image ( or three- dimensional images obtained by laser scan).

A face recognition system generally consists of 4 modules - detection, alignment, feature extraction, and matching.

Face Recognition from video.Register w.r.t a SubspaceSelecting the most discriminative samples.

*Classification A face recognition system is expected to identify faces present in imagesand videos automatically. It can operate in either or both of twomodes:Face verification (or authentication): involves a one-to-one match that compares a query face image against a template face image whose identity is being claimed.

Face identification (or recognition): involves one-to-many matches that compares a query face image against all the template images in the database to determine the identity of the query face.

First automatic face recognition system was developed by Kanade 1973.

EE465: Introduction to Digital Image Processing Copyright Xin Li'2003*The Basic IdeaWe should easily recognize the point by looking through a small windowShifting a window in any direction should give a large change in intensity

EE465: Introduction to Digital Image Processing Copyright Xin Li'2003

Experimentation and Results

System Overview

The procedure for Face recognition is as follows. 1. Pre processing: The image is rescaled and the noise is reduced, contrast was normalized with histogram equalization.. 2. RobustPCA: The images then are applied with RobustPCA for reduction in dimensionality and there by reducing complexity. 3. Modified RBFN: The outputs of Robust PCA are applied to RBFN for classification , separation of faces and non faces and for training.

REFRENCES[1] M. Turk, A. Pentland, Eigen faces for Recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, Win. 1991, pp. 71-86 [2] Discriminant analysis for recognition of human face images Kamran Etemad and Rama Chellappa [3] MPCA: Multilinear Principal Component Analysis of Tensor Objects, Haiping Lu, Student Member, IEEE, Konstantinos N. (Kostas) Plataniotis, Senior Member, IEEE, and Anastasios N. Venetsanopoulos, Fellow, IEEE [4] Face detection Inseong Kim, Joon Hyung Shim, and Jinkyu Yang

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