Person Identification Security System

Created by1. Juhi Achhra2. Prachi Chauhan3. Ninad Dolas4. Devendra Sanflikar

Project GuideProf. Vrushali Purandare

Organization of Presentation

• Objective• Problem Definition• Introduction• Literature Survey• Proposed Model• Future Scope• References

objective

• To develop robust and efficient face detection technique using Skin-Tone detection, Edge detection & Template matching.

• To obtain transformed images and prepare a database using Eigen Vector.

• To evaluate face recognition using Eigenface algorithm.

Problem definition

• In most of the incidents, only the suspect’s face gets captured in the CCTV Cameras.

• But the authorities are unable to recognize the suspect.

• So our project gives a solution to identify the suspect by Face Recognition.

• It can be used in Banks, Restaurants, Corporate Areas, etc.

IntroductionA facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. The human face plays a major role in conveying identity and emotion. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. A notable advantage of facial recognition over other biometric recognition methods is that it is less cumbersome for end users. Real time application of face recognition theory and is formulated based on still or video images captured either by digital camera or by a webcam. The faces considered here for comparison are still faces. Here we have developed a Matlab code initializing the webcam of a laptop, capturing the image and comparing it with the database of images present in the laptop.

Literature Survey

Algorithms for Face Recognition• Holistic Approach

– Entire face act as raw input– Optimal variance of pixel data

• Feature Based Approach– Local Features extracted– Features act as vector of geometric features

Literature Survey

• A. Feature base approach In feature based approach the local features like nose, eyes are segmented and it can be used as input data in face detection to easier the task of face recognition.

• B. Holistic approach In holistic approach the whole face taken as the input in the face detection system to perform face recognition.

• C. Hybrid approach Hybrid approach is combination of feature based and holistic approach. In this approach both local and whole face is used as the input to face detection system.

Literature Survey

Histogram of skin and non skin pixels is determined.

Lightening compensation is employed.

Face interpreted as sum of chrominance and luminance.

Illumination Variant.

Skin tone Matching

Uses Physiological face image.

Face image is converted into gray level picture.

Image is not normalized.

Face is combination of binary edge maps.

Illumination Variant.

Edge Matching

Divides face into feature vectors.

Feature vectors are converted to co-variancematrix.

Image is normalized.

Face is combination of Eigen vectors.

Illumination Invariant.

Eigen Face

Proposed Model

Face Detection

• A process for face detection, which involves multi-resolution template matching, region clustering and colour segmentation, works with high accuracy, and gives good statistical results with training images.

• Given the generality of the images and the templates used, the assumption would be that the implementation works well on other images, regardless of the scene lighting, size of faces or type of faces in the pictures.

PROCESS• Template matching is performed first to

find the regions of high correlation with the face and eyes templates.

• Subsequently, using a mask derived from colour segmentation and cleaned by texture filtering and various binary operations, the false and repeated hits are removed from the template matching result.

• The output of this process is then passed to a clustering procedure, where points are within a certain Euclidean distance from one another will be clustered into one point.

• The whole process will then be repeated at a different scale/resolution.

• The outputs from each resolution are then recombined into a single mask.

DETECTION FLOWCHART

• Live video preview will be monitored on the screen.

• If the supervisor clicks on the face of the suspect only then the recognition process starts.

• The Algorithm includes Skin tone detection, Edge detection, Template Matching & Eigen Vector conversion.

START

If Mouse

Click

Algorithm

NO

YES

Skin-Tone Matching• Skin color model-based approaches build a

skin color model using Gaussian normal distribution since color is one of the most widely used visual features in face detection.

• Specifically, said models convert the color image into an appropriate color space, such as HSI, YCbCr, or YIQ, to find skin color.

• These color spaces are more robust to the • lighting conditions than the RGB color space

and therefore are suitable for face detection under different illuminations.

• The mean and covariance matrix of the skin color are then computed from the skin colors. Finally, the results of this computation are used to find the likelihood that each pixel in the input image is, indeed, a skin color.

Edge Matching • Edges define the boundaries between

regions in an image, which helps with segmentation and object recognition.

• The algorithm describes a new technique based on line edge maps (LEM)

• In order to measure the similarity of human faces the face images are firstly converted into gray-level pictures.

• The images are encoded into binary edge maps using Sobel edge detection algorithm to accomplish face recognition.

TEMPLATE MATCHING• The discrete convolution of two functions f(x, y)

and h(x, y) of size M × N is denoted by f(x, y) ∗h(x, y) and is defined as the following expression:

• For feature detection, simple eye and mouth templates were used.

The templates search for the eye and the mouth location

Face Recognition

• Most of the face recognition literature dealt with local and intuitive features, such as distance between eyes, ears and similar other features.

• This system was found to be inefficient as it took significantly more time for object recognition.

• The system will use an Information Theory approach wherein the most relevant face information is encoded in a group of faces that will best distinguish the faces.

• It transforms the face images in to a set of basis faces, which essentially are the principal components of the face images.

• This is particularly useful for reducing the computational effort.

EIGENFACE DATABASE• Algorithm for creating Eigenface Database:1. Obtain ‘M’ training images I1,I2, … ,IM. It is very important that the images are centred.2. Represent each image Ii as a vector Ʈi as discussed below.

3. Find the average face vector Ψ.

4. Subtract the mean face from each face vector Ʈi to get a set of vectors Φi. The purpose of subtracting the mean image from each image vector is to be left with only the distinguishing features from each face and “removing” in a way information that is common.

EIGENFACE DATABASE5. Find the Covariance matrix C.

6. We now need to calculate the Eigenvectors ‘ui’ of C .

7. Instead of the Matrix consider the matrix . Remember A is a matrix, thus is a matrix. If we find the Eigenvectors of this matrix, it

would return M Eigenvectors, each of Dimension M x 1, let’s call these Eigenvectors ‘vi’. Now from some properties of matrices, it follows that: ‘ui=Avi’. We have found out ‘vi’ earlier. This implies that using ‘vi’ we can calculate the M largest Eigenvectors of . Remember

that M<<N^2 as M is simply the number of training images.

8. Find the best M Eigenvectors of C= by using the relation discussed above.

That is: ui=Avi. Also keep in mind that||ui||=1.

9. Select the best M Eigenvectors, the selection of these Eigenvectors is done heuristically.

EIGENFACE DATABASE

Database of Faces Eigenfaces of Database

Face Recognition• If an unknown probe face is to be recognized then:1. We normalize the incoming probe Ʈ as:

Φ=Ʈ – Ψ.2. We then project this normalized probe onto the Eigen space (the

collection of Eigenvectors/faces) and find out the weights.

3. The normalized probe can then simply be represented as:

Face Recognition Face as a Linear Combination of Database Faces

FUTURE SCOPE

• Live as well as previously recorded video can be used for person identification.

• Even if the face is blur, we can identify the person by Image Enhancement Techniques.

• With the use of Hi-Tech cams, the speed of recognition can be further increased.

• Advanced Identification Security Systems can also be used for classified operations.

References1. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-

2, Issue-5, November 2012 2. FACE DETECTION THROUGH TEMPLATE MATCHING AND COLOR SEGMENTATION By Scott

Tan Yeh Ping (yptan@stanford.edu),Chun Hui Weng (cwengc@stanford.edu),Boonping Lau (bppower@stanford.edu) (EE 368 Final Project)

3. M Sudarshan*, P Ganga Mohan and Suryakanth V Gangashetty Speech and Vision Lab, International Institute of Information Technology, Hyderabad, Andhrapradesh, India - 50032.

4. Segmentation Algorithm for Multiple Face Detection in Color Images with Skin Tone Regions using Color Spaces and Edge Detection TechniquesH C Vijay Lakshmi, S. PatilKulakarni,

5. Robust Face Detection Using Template Matching Algorithm by Amir Faizi(University of Toronto)(Copyright c 2008 by Amir Faizi)

6. Smart Survelliance System, Jigar Gada 1, Jaimin Kakadiya 2, Darshit Morakhia 3 and Vishakha Kelkar U.G. Student, Electronics and Telecommunication Department, Assistant Professor, Electronics and Telecommunication Department, DJSCOE, Vile-Parle (W), Mumbai.

Thank You…..