Face Recognition

Human Computer Interaction

By: Beltaief Ines & Hadj Sassi Mohamed Saif Eddine

OUTLINE face recognition DefintionDifferent approachesMost used Techniques applications

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Introduction Over the last ten years or so, face recognition has become a

popular area of research in computer vision and one of the most successful applications of image analysis and understanding.

It is the general opinion that advances in computer vision research will provide useful insights to neuroscientists and psychologists into how human brain works, and vice versa.

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General Idea

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Face RecognitionA set of two task:Face Identification: Given a face image that

belongs to a person in a database, tell whose image it is.

Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database.

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Similar ProjectsFace Detection Based on Skin

Color and Edge Detection

Facial Expression Recognition

Computer Interaction Using Color Detection Techniques

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ApprochesFacial Expression Recognition:

Facial expression recognition follows the research framework of pattern recognition. This is composed of three steps:

1)detection of face2)feature (facial) extraction 3)expression classification

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Face Detection Based on Skin Color and Edge Detection:

The most proficient way of extracting skin regions is through the use of color cues which allows easy localization of the potential facial regions without considering the texture and geometrical attributes.

Most current skin detection methods that draw on color properties are pixelbased , where every pixel is categorized individually and independently as skin or “non-skin”.

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Approches

HCI Using Color Detection Techniques

The main objective of Method proposed is based on real time controlling the motion of mouse in windows .

It’s according to the motion of hand and fingers by calculating the change in pixels values of RBG colors from a video, (without using any ANN training)*, to get exact sequence of motion of hands and fingers.Artificial Neural Networks : simplified models based on biological neurons , their design

enables them to process information in a similar way to our own biological brains, by drawing inspiration from how our own nervous system functions.

➔ This makes them useful tools for solving problems like facial recognition.

video capturing

image processing

Pixel Extraction

color detection

controlling position of mouse printer

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Approches

TECHNIQUESFace detection Algorithm

Face detection is a computer technique that determines

the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and other bodies.

Some systems detect and locate faces at the same time, others first perform a detection routine and then, try to locate the face.

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PCADerived from Karhunen-Loeve's transformation. Given an s-dimensional

vector representation of each face in a training set of images, Principal Component Analysis (PCA) tends to find a t-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space.LDA

Linear Discriminant Analysis (LDA) finds the vectors in the underlying space that best discriminate among classes. For all samples of all classes the between-class scatter matrix SB and the within-class scatter matrix SW are defined

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TECHNIQUES

Kernel methodsThe face manifold in subspace need not be

linear. Kernel methods are a generalization of linear methods. Direct non-linear manifold schemes are explored to learn this non-linear manifold.

Trace transformThe Trace transform, a generalization of the Radon transform, is a new tool for image processing which can be used for recognizing objects under transformations, e.g. rotation, translation and scaling. 10

TECHNIQUES

Template matching

Bottom-up approach: Detect facial features (eyes, nose,mouth, etc)

Facial features: edge, intensity, shape, texture, color, etc

Template Matching 12

TECHNIQUES

Face Recognition applications Easy people tagging

Facebook’s automatic tag suggestion feature, which used face recognition to suggest people you might want to tag in your photos.

GamingImage and face recognition is bringing a whole new dimension to gaming. Microsoft’s Kinect’s advanced motion sensing capabilities have given the Xbox 360 a whole new lease of life and opened up gaming to new audiences by completely doing away with hardware controllers.

Price comparisonIn a shop and want to find out if you’re getting a good deal? Just snap a picture of the product you want to buy and apps like Google Shopper and eBay-owned RedLaser will give you the kind of knowledge shopkeepers wish you didn’t have access to.

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Image SearchGoogle recently introduced the ability to search for images by comparing them to others. By uploading an image or giving Google an image URL, it will show you where that image is used on the Web, and display similar images too SecurityFace recognition could one day replace password logins on our favourite apps – imagine logging in to Twitter with your face, for example. Making mental notesIf you take visual notes of things you want to remember using your mobile phone’s camera, you could do with taking a look at Deja Vu. This app helps you organise images you’ve taken as reminders, letting you add notes, tags and location data

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Face Recognition applications

thanks for your attention

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