Presented By Bhargav (08BQ1A0435)

 Images play an important role in todays information because A single image represents a thousand words.  Google's image search, where we can easily search for images using keywords. Getting the computer to understand the semantics inside of images isn't easy. The reason for this is simply because the computer isn't able to understand the context. But

Face Detection Face Recognition

 Face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding.  A 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.

 Detection accuracy affects the recognition stage  Key issues: ◦ Correct location of key facial features (e.g. the eye corners) ◦ False detection ◦ Missed detection

 Different methods of face recognition. ◦ Feature extraction methods ◦ Holistic methods ◦ Hybrid methods

Feature extraction is the task where we locate facial features, – Eg: the eyes, the nose, and the chins etc. This task may be performed after the face detection task Or recognition time. big challenge for feature extraction methods is feature “restoration“. – Facial features are invisible according to the large variation.

 This method is widely used to create individual vectors for each person in a system, the vectors are matched when an input image is being recognized.

 Holistic methods uses the whole face region as the input to a recognition system.  focuses a holistic method using eigenfaces to recognize still faces.

1.The first stage is to insert a set of images into a database, these images are called the training set, this is because they will be used when we compare images and when we create the eigenfaces. 2.The second stage is to create the eigenfaces. Eigenfaces can now be extracted from the image data by using a mathematical tool called Principal Component Analysis (PCA). 3.When the eigenfaces have been created, each image will be represented as a vector of weights. 4.The system is now ready to accept incoming queries.

5.The weight of the incoming unknown image is found and then compared to the weights of those already in the system. If the input image's weight is over a given threshold it is considered to be unknown. The identification of the input image is done by finding the image in the database whose weights are the closest to the weights of the input image. The image in the database with the closest weight will be returned as a hit to the user of the system.

 Hybrid face recognition systems uses a combination of both holistic and feature extraction methods.  Hybrid method of face recognition by using 3D morphable model. The model makes it possible to change the pose and the illumination on the face.

 Took face recognition to a new level. By being able to use a morphable 3D model to create synthetic images has proven to give good results. It is a very applicable approach that solves many of the problems. system achieved a recognition rate of 90%.

 when comparing a database image with an input image. The main concern is of course that all images of the same face are heterogeneous.  When image databases are created they contain good scenario images.  concerning deferent facial expressions as well. The system must be able to know that two images of the same person with deferent facial expressions actually is the same person. makeup, posing positions, illumination conditions, and comparing images of the same person with and without glasses.

Fastest and safest method of tracking employee time and attendance. Easy to install and use. Cost saving and convenient way of time tracking. Provide easy and efficient way of recording attendance. Easily manage employee time and attendance profiles. Get rid of buddy punching. Also manage employee payroll record. On-demand time attendance record for reference. Easily customizable as per your requirement.  Face Recognition based Time Attendance System

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Applications Available in Market  Facial Recognition PC Security Logon provides a simple but effective option. The integration of Logon and PC camera provides access only when a live-fed face image of authorized user is detected, thus effectively preventing unauthorized access. Logon is a non- invasive technology that does not require physical contact.

Applications Available in Market  Face Biometric Login Through Web Embeddable in any web page Global Face Authentication capability Free version available View of authenticated clients Messaging to Clients possible Remote Backup/Restore  Google's Picasa  Facial Recognition Software in Online Gaming and Crime Prevention

Introduction of face Recognition How facial recognition works. Face detection and recognition. Different approaches of face Recognition. Feature extraction methods Holistic methods Hybrid methods Problems

 W. Bledsoe. Man-machine facial recognition  J. Huang, B. Heisele, and V. Blanz. Component based face recognition with 3d morphable models  T. Kanade. Computer Recognition of Human Faces  M. D. Kelly. Visual identification of people by computer.  - Application of face recognition