real time eye detection (2) edited final
TRANSCRIPT
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REAL TIME EYE DETECTION
BY- GUIDANCE BY-
MADHUR DHAMNE DR. S. P. NAROTE
ROHIT DHAYGUDE
TUSHAR DHULE
DEPT. OF ELECTRONICS & TELECOMMUNICATION ENGG.SINHGAD COLLEGE OF ENGINEERING, PUNE
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Objectives
To detect the EYE from the face present in the field of view of CAMERA
To move the camera to align itself to capture eye
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Introduction
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Roadmap
What is Digital Image Processing ? Fundamentals steps in DIP
Image acquisition Image enhancement Image restoration Color image processing Compression Morphological processing Segmentation Representation and description Recognition
Feature Extraction Feature Detection and Classification
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Fundamental Steps in DIP
Image acquisition- is the first process which involves preprocessing such as scaling.
Image enhancement- this is bringing out obscured detail or highlighting certain features of interest in an image. This technique deals with a number of mathematical functions such as the Fourier Transform.
Image restoration- it improves the appearance of an image but is objective in the sense that this technique tends to be based on mathematical or probabilistic models of image degradation.
Color image processing- this is used as a basis for extracting features of interest in an image.
Wavelets- are the foundation for representing images in various degrees of resolution.
Compression- deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it.
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(continued…)
Morphological processing- deals with tools for extracting image components that are useful in the representation and description of shape.
Segmentation- partitions an image into its constituent parts or objects. Representation and description- representation is necessary for
transforming raw data into a form suitable for subsequent computer processing. Description, also known as feature selection, deals with extracting attributes that result in some quantitative information of interest.
Recognition- assigns a label to an object based on its descriptors.
Feature Extraction- this is an area of image processing which involves using algorithms to detect and isolate various desired portions of a digitized image or video stream.
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Image Enhancement
Original
Histogram Example
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Histogram Example (cont… )
Poor contrast
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Histogram Example (cont… )
Poor contrast
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Histogram Example (cont… )
Enhanced contrast
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Smoothing and Sharpening Examples
Smoothing Sharpening
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Image analysis
Image analysis is to identify and extract useful information from an image or a video scene, typically with the ultimate goal of forming a decision.
Image analysis is the center piece of many applications such as remote sensing, robotic vision and medical imaging.
Image analysis generally involves basic operations: Pre-processing, Object representation, Feature detection, Classification and interpretation.
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Image Segmentation
Image segmentation is an important pre-processing tool. It produces a binary representation of the object with features of interest such as shapes and edges.
Common operations include: Thresholding: to segment an object from its background
through a simple pixel amplitude based decision. Complicated thresholding methods may be used when the background is not homogeneous.
Edge detection: to identify edges of an object through a set of high-pass filtering. Directional filters and adaptive filters are frequently used to achieve reliable results.
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Segmentation Examples
Thresholding Edge detection
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Feature Extraction
This is an area of image processing that uses algorithms to detect and isolate various desired portions of a digitized image.
What is a Feature?
A feature is a significant piece of information extracted from an image which provides more detailed understanding of the image.
Examples of Feature Detections Detecting of faces in an image filled with people and other
objects Detecting of facial features such as eyes, nose, mouth Detecting of edges, so that a feature can be extracted and
compared with another
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Feature Detection and Classification
Feature detection is to identify the presence of a certain type of feature or object in an image.
Feature detection is usually achieved by studying the statistic variations of certain regions and their backgrounds to locate unusual activities.
Once an interesting feature has been detected, the representation of this feature will be used to compare with all possible features known to the processor. A statistical classifier will produce a feature type that has the closest similarity (or maximum likelihood) to the testing feature.
Data collection and analysis (or the training process)have to be performed at the classifier before any classification.
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CAMERA COMPUTER
MECHANICAL ASSEMBLY
CONTROLLING CAMERA FOCUS
MICROPROSESSORCIRCUIT
Block Diagram
INPUT
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Phases
PHASE 1 CAMERA MOVEMENT
PHASE2 FACE DETECTION
PHASE3 LOCATE EYE
PHASE4 CAPTURE EYE
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Camera movement
Mechanical Assembly Gears & links
Motion control According to co-ordinates
Servo motors Load consideration
Controller/Processor Speed considerations
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Face Detection
Face Detection
What is face detection?
Importance of face detection
Different approaches
One example
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What is Face Detection?
Given an image, tell whether there is any human face, if there is, where is it.
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Importance of Face Detection
Required in any face recognition system
Most important in any SURVEILLANCE system
Extracting facial features
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Different Approaches
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Features based Approach
Face color is a unique feature
This can be used to detect a face.
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Face Circle fitting Algorithm
The continuous images as the input The the Skin Color pixel extracted Similar color pixel blocks identification Block grouping Final boundary of face region Face circle fitting
(face is considered as the circular object) Face co-ordinates as the centre of the circle
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Flow-chart
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Details of the algorithm
The algorithm uses HSI color modeling the H value of pixel decides the skin color pixel
The value of H falling in the range HLo, HHi are selected Blocks of pixels are examined
group of 4 blocks area more than 1/3rd then skin color
The boundaries of face region are optimized
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The holes are filled Face circle is fitted
centre location fixed Cvc= Ftop + R Chc is decided by vertical positions Pv[i] The maximum value of Pv[i] gives the Chc
the radius is calculated using boundary values R= (Ftop - F bottom)/2
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Color Segmentation Algorithm
Color Segmentation
Captured image => RGB color space Affected by lightning condition Remedy : use of YCbCr color space Y component => luminance Cb, Cr component => chrominance
RGB to YCbCr conversion Y = 0.299R + 0.587G + 0.114B Cb = -0.169R - 0.332G + 0.500B Cr = 0.500R - 0.419G - 0.081B
Result of color segmentation
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Image Segmentation
Need : to obtain perfect binary image A three step process:
Step 1: removal of small black and white regions
Step 2: edge detection using Robert Cross Operator
Step 3: integration of step 1 and step 2
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Step 1 Elimination of small black holes on white region Removal of white region less than minimum face area
Small region eliminated
image
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Step 2 Robert cross algorithm
Performs gradient measurement on binary image Highlights gradient of high magnitude Converts highlighted region into black lines by connecting
adjacent pixels Gradient magnitude |G | = |P1 – P4 | + |P2 – P3 |
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Edges detected by Robert cross operator
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Integration of step1 and 2 Small black and white regions removed Clear view of skin region
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Image matching Template eigen image generation Correlating test image with eigen image template
Drawbacks All the faces are vertical and have frontal view Images are captured under same lightning conditions
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Eye Detection
Importance of Eye Detection
Challenges
Different Approaches
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Eye Detection(continued…)
Importance of Eye Detection Man machine interaction technology Monitoring human vigilance Assisting people with disability
Challenges Eye closure Eye occlusion Variability in scale and location, different Lighting conditions Face orientation
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Different Approaches
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Dark Area Extraction Algorithm
The dark pixels extraction
Intensity comparison
Change the boundaries
Detect the eye area
Mark Eyes
Send co-ordinates to processor
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Flow Chart
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Details of Algorithm
Eye positioning,(a) source image,(b) eye pixel extraction,(c) eye pixel grouping,(d) unreasonable pixel removal,(e) horizontal position locating,(f) nose pixel and eyebrow pixel removal,(g) vertical position location,(h) eye marking.
Eye Positioning
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Convert the captured image in YCbCr color space
Build two separate eye maps EyeMapC EyeMapL
Above two eye maps are combined to get a single eye map.
Parallel Eye Detection
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Eye Map C construction In YCbCr color space eye region have higher Cb value and lower Cr
value EyeMapC = 1/3 ((Cb)2 + (Cr)2 + (Cb/Cr))
Eye Map L construction Design of dilation and erosion operator To emphasis on bright and dark area around eyeEye map L = Y(x , y) + g(x , y)
Y(x , y) * g (x , y)
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Combining eye map C and eye map L
Table shows the experiment results
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Results(Comparison Chart)
METHOD APPROACH DETCTION RATE
(DATABASE)
ADVANTAGE DISADVANTAGE
FACE DETECTION
FACE CIRCLE FITTING
COLOR BASED(HSI) 85 -92 %
BACKGROUND FILLTERING IS FAST
RGB HSI REQUIRED,NO DIRECT FUNCTION
COLOR SEGMENTATION
ALGORITHMCOLOR BASED
(YCbCr)
90%FLASE
DETECTION 5%
LIGHTNING EFFECT IS REDUCED, LESS COMPUTATION TIME
GENDER RECOGNITION
85%
SPEED OF PROCEESING INCREASE DUE TO PARRALEL IMPLEMENTATION
IMAGE NEEDS TO BE FRONTAL,SSHOLUD NOT BE OCCLUDED BY GLASS ETC
EYE DETECTION
BUILDING EYE MAP
DARK AREA EXTRACTION
FEATURE INVARIANT
85 -92 % COMPUTATIONAL TIME IS LESS
EFFECT OF LIGHTNING CONDITION
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Mechanical Assembly
Microprocessor system Motors systems Links and Gears Camera & Mountings
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Assembly(how it will look/function)
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Design Considerations
1. Camera Weight (around 1kg.)
2. Minimum displacement required Vertical & Horizontal Gear ratio calculation
3. Mobility with connecting cables The cables should be routed to achieve maximum
mobility for camera movement
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Embedded system
ARM
Motor Drivers
PC interfacing Circuit
(USB 2.0/RS-232)
Manual Controls
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ARM
ARM 7 TDMI Designed to be small & reduce the power consumption High code density Hardware debug technology within the processor High speed Flash memory(32 to 128 kB) Large buffer size and high processing power Various 32-bit timer Supports USB 2.0 Full-speed device, multiple UARTs, SPI, SSP
to I2C-bus Supports In-System Programming/In-Application Programming
(ISP/IAP)
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Proposed Work(Planning Sheet)
ActivitiesActivities Semester –ISemester –I Semester -IISemester -II
1. Basics of Image 1. Basics of Image ProcessingProcessing
2. Literature Survey2. Literature Survey
3. Software Study 3. Software Study
4.Development of 4.Development of algorithmalgorithm
5.Implementation of 5.Implementation of prototype prototype
6. Optimization analysis6. Optimization analysis
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References
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[4] F.Y. Shih, C. Chuang, Automatic extraction of head and face boundaries and facial features, Information Sciences 158 (1) (2004) [117–130].
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[9] R. Xiao, M. Li, H. Zhang, Robust multipose face detection in images, IEEE Transactions on Circuits and Systems for Video Technology 14 (1) (2004) [31–41].
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[17] S. Phimoltares, C. Lursinsap, K. Chamnongthai, Locating essential facial features using neural visual model, in: Proceedings of the First IEEE International Conference on Machine Learning and Cybernetics, 2002, pp. [1914–1919].
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Thank You…!