Interactive Face Recognition (IFR) Nishanth Vincent Fairfield University Advisor: Professor Douglas A. Lyon, Ph.D.

Slides:



Advertisements
Similar presentations
CDS 301 Fall, 2009 Image Visualization Chap. 9 November 5, 2009 Jie Zhang Copyright ©
Advertisements

Face Recognition Method of OpenCV
Introduction to Morphological Operators
Development of a Compact Cluster with Embedded CPUs Sritrusta Sukaridhoto, Yoshifumi Sasaki, Koichi Ito and Takafumi Aoki.
Quadtrees, Octrees and their Applications in Digital Image Processing
Morphology Structural processing of images Image Processing and Computer Vision: 33 Morphological Transformations Set theoretic methods of extracting.
Programming Assignment 2 CS308 Fall Goals Improve your skills with using templates. Learn how to compile your code when using templates. Learn more.
Interactive Visual System By Arthur Evans, John Sikorski, and Patricia Thomas.
Quadtrees, Octrees and their Applications in Digital Image Processing
Facial Features Extraction Amit Pillay Ravi Mattani Amit Pillay Ravi Mattani.
Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.
Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP.
California Car License Plate Recognition System ZhengHui Hu Advisor: Dr. Kang.
2007Theo Schouten1 Morphology Set theory is the mathematical basis for morphology. Sets in Euclidic space E 2 (or rather Z 2 : the set of pairs of integers)
Feature extraction Feature extraction involves finding features of the segmented image. Usually performed on a binary image produced from.
Cambodia-India Entrepreneurship Development Centre - : :.... :-:-
GUIDED BY: C.VENKATESH PRESENTED BY: S.FAHIMUDDIN C.VAMSI KRISHNA ASST.PROFESSOR M.V.KRISHNA REDDY (DEPT.ECE)
Prepared by Careene McCallum-Rodney Hardware specification of a computer system.
Chapter 3.1:Operating Systems Concepts 1. A Computer Model An operating system has to deal with the fact that a computer is made up of a CPU, random access.
Information Extraction from Cricket Videos Syed Ahsan Ishtiaque Kumar Srijan.
Introduction to Computers By: Najam Khan What we will learn about: Hardware: The term used to describe the physical parts of a computer. Ex. The box,
Chapter 9.  Mathematical morphology: ◦ A useful tool for extracting image components in the representation of region shape.  Boundaries, skeletons,
Automated Face Detection Peter Brende David Black-Schaffer Veni Bourakov.
Implementing Codesign in Xilinx Virtex II Pro Betim Çiço, Hergys Rexha Department of Informatics Engineering Faculty of Information Technologies Polytechnic.
Virtual Fences for Controlling Cows Presenter: Serafettin Tasci.
Joon Hyung Shim, Jinkyu Yang, and Inseong Kim
Morphological Image Processing
1 Webcam Mouse Using Face and Eye Tracking in Various Illumination Environments Yuan-Pin Lin et al. Proceedings of the 2005 IEEE Y.S. Lee.
Quadtrees, Octrees and their Applications in Digital Image Processing.
Cellular Automata based Edge Detection. Cellular Automata Definition A discrete mathematical system characterized by local interaction and an inherently.
Video Segmentation Prepared By M. Alburbar Supervised By: Mr. Nael Abu Ras University of Palestine Interactive Multimedia Application Development.
Digital Image Processing CSC331 Morphological image processing 1.
1 Research Question  Can a vision-based mobile robot  with limited computation and memory,  and rapidly varying camera positions,  operate autonomously.
Eurecom, 6 Feb 2007http://biobimo.eurecom.fr Project BioBiMo 1.
Eye regions localization Balázs Harangi – University of Debrecen Ciprian Pop – Technical University of Cluj-Napoca László Kovács – University of Debrecen.
Digital Image Processing CSC331 Morphological image processing 1.
By Using Statistical Models to Detect the Characteristics of Human Face 利用統計模型在彩色圖像 中偵測人臉特徵 逄霖生 中國文化大學 電機工程學系.
Supervisor: Nakhmani Arie Semester: Winter 2007 Target Recognition Harmatz Isca.
CS654: Digital Image Analysis
Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.
EE368 Digital Image Processing Face Detection Project By Gaurav Srivastava Siddharth Joshi.
CDS 301 Fall, 2008 Image Visualization Chap. 9 November 11, 2008 Jie Zhang Copyright ©
Face Detection Using Color Thresholding and Eigenimage Template Matching Diederik Marius Sumita Pennathur Klint Rose.
TOPIC 12 IMAGE SEGMENTATION & MORPHOLOGY. Image segmentation is approached from three different perspectives :. Region detection: each pixel is assigned.
BYST Morp-1 DIP - WS2002: Morphology Digital Image Processing Morphological Image Processing Bundit Thipakorn, Ph.D. Computer Engineering Department.
Machine Vision ENT 273 Hema C.R. Binary Image Processing Lecture 3.
EE368: Digital Image Processing Bernd Girod Leahy, p.1/15 Face Detection on Similar Color Images Scott Leahy EE368, Stanford University May 30, 2003.
Morphological Image Processing (Chapter 9) CSC 446 Lecturer: Nada ALZaben.
Morphological Image Processing
Progress check Learning Objective: Success Criteria : Can identify various input and output devices - Level 4 – 5 Can identify all the major items of hardware.
Face Detection – EE368 Group 10 May 30, Face Detection EE 368 Group 10 Waqar Mohsin Noman Ahmed Chung-Tse Mar.
License Plate Recognition of A Vehicle using MATLAB
Content Based Coding of Face Images
EE368 Final Project Spring 2003
SMART CAMERAS AS EMBEDDED SYSTEM SMVEC. SMART CAMERA  See, think and act  Intelligent cameras  Embedding of image processing algorithms  Can be networked.
FINGERTEC FACE ID FACE RECOGNITION Technology Overview.
An Embedded Wireless Mini-Server with Database Support Presented by: Amit Kumar.
Identify internal hardware devices (e. g
Introduction to Skin and Face Detection
Face Detection EE368 Final Project Group 14 Ping Hsin Lee
Scott Tan Boonping Lau Chun Hui Weng
Dingding Liu* Yingen Xiong† Linda Shapiro* Kari Pulli†
Statistical Approach to a Color-based Face Detection Algorithm
Binary Image Analysis used in a variety of applications:
Interactive Visual System
Binary Image processing بهمن 92
Electronic Door Unlock with Face Recognition
Speaker: YI-JIA HUANG Date: 2011/12/08 Authors: C. N
Midterm Exam Closed book, notes, computer Similar to test 1 in format:
Binary Image Analysis used in a variety of applications:
Presentation transcript:

Interactive Face Recognition (IFR) Nishanth Vincent Fairfield University Advisor: Professor Douglas A. Lyon, Ph.D.

Interactive Face Recognition The Interactive face recognition system is a stand-alone GUI implementation on the Sharp Zaurus SL-6000L The Zaurus is provided with a 400MHz processor, 64 MB RAM, and Compact Flash and Serial Device ports It is equipped with a Sharp CE-AG06 camera attachment which is inserted into the Compact Flash port & a wireless network card The operating system is Embedded Linux with Personal Java support.

Sharp Zaurus – PDA

Camera The source code for the camera is in C, so we call the executable at runtime using java. private void camera() { try { Runtime.getRuntime (). exec ("/home/QtPalmtop/bin/./sq_camera"); } catch (IOException ioe) { ioe.printStackTrace (); }

Problem Definition We are given an input scene and a suspect database Goal is to find a set of possible candidates Challenge is to run the algorithm on the given embedded hardware.

Skin detection – YCbCr Color Model Skin detection was performed in the YCbCr color model In this color model, the luminance component is separated from the color components

GUI for Zaurus

Threshold in YCbCr if ( (Cb[x][y] < 173) && (Cb[x][y] > 133) && (Cr[x][y] < 127) && (Cr[x][y] > 77) ) setPixel(x, y, 255); else setPixel(x, y, 0); }

Skin Detected Image

Morphological operator :-Dilation Dilation is defined as a morphological operator, which is usually applied to binary images. The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels

Dilated Image

Morphological operator :-Erosion Erosion is defined as a morphological operator which is also applied to binary images. It is used to erode away the boundaries of regions of foreground pixels. Thus the areas of foreground pixels shrink in size, and holes within those areas become larger

Eroded Image

Face detection

Face Database for Face Recognition

PCA-principal component analysis This algorithm treats face recognition as a two-dimensional recognition problem, It takes advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristics Face images are projected onto a feature space ('face space') that best encodes the variation among known face images

Face Recognition

Conclusion we have presented an interactive face recognition algorithm on the embedded device. Our work is significantly novel compared to the previous work for the fact that we are able to match the faces from the scene in an interactive time and that our algorithm is able to run on the given embedded hardware