FACE RECOGNITION USING PRINCIPLE COMPONENT ANALYSIS

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FACE RECOGNITION USING PRINCIPLE COMPONENT ANALYSIS: 

FACE RECOGNITION USING PRINCIPLE COMPONENT ANALYSIS UNDER THE ESTEEMED GUIDANCE OF Mr.K.Kalyan Babu M.E Assistant Professor Dept of Electronics & Communication Engg GITAM UNIVERSITY Md. Ubeadulla 2007ECE131 GITAM UNIVERSITY L.Rajasekhar Reddy 2007ECE126 GITAM UNIVERSITY

FACE RECOGNITION: 

FACE RECOGNITION The identification of a person by their facial image can be done in a number of different ways such as by capturing an image of the face in the visible spectrum using an inexpensive camera or by using the infrared patterns of facial heat emission. Using a wide assortment of cameras, the visible light systems extract features from the captured image(s) that do not change over time while avoiding superficial features such as facial expressions or hair. Several approaches to modeling facial images in the visible spectrum are Principal Component Analysis,Local Feature Analysis, neural networks, elastic graph theory, and multi-resolution analysis. Major benefits of facial recognition are that it is non-intrusive, hands-free , continuous and accepted by most users.

APPLICATIONS OF FACE RECOGNITION: 

APPLICATIONS OF FACE RECOGNITION It is generally applicable to a variety of applications , and as such accepts color or black and white images both still and video. With PC-attached cameras for computer logon from a smart-card stored database. On broadcast video for indexing from a database of enrolled TV presenters . Components of the system have also been used in a number of other projects such as audio-visual speech recognition (visual lip reading to enhance acoustic speech recognition) and user intention determination (using visual cues to understand the user, particularly to whom speech is being addressed).

Slide 4: 

Principal Component Analysis (PCA) is a dimensionality reduction technique based on extracting the desired number of principal components of the multi-dimensional data. The purpose of PCA is to reduce the large dimensionality of the data space to the smaller intrinsic dimensionality of feature space ,which are needed to describe the data economically . The first principal component is the linear combination of the original dimensions that has the maximum variance ; T he n- th principal component is the linear combination with the highest variance, subject to being orthogonal to the n -1 first principal components . PRINCIPLE COMPONENT ANALYSIS

EIGEN FACES: 

EIGEN FACES The basis vectors constructed by PCA had the same dimension as the input face images, they were named Eigen faces. A simple approach to extracting the information contained in an image of face is to somehow capture the variation in a collection of images, independent of any judgment of features, and use this information to encode and compare individual face images. These eigenvectors can be thought of as a set of features that together characterize the variation between face images. Each image location contributes more or less of each eigenvector, so that we can display the eigenvector as a sort of ghostly face which we call an Eigen face.

EXAMPLE OF EIGEN FACES: 

EXAMPLE OF EIGEN FACES Example of Face DataBase of 2 persons Example of Eigen Faces

CASES OF IMAGE INPUT: 

CASES OF IMAGE INPUT Thus there are four possibilities for an input image and pattern vector: 1.)Near face space and near face class, 2.)Near face space but not near a known face class, 3 .)Distant from face space and near a face class, and 4.)Distant from face space and not near a known face class. In the first case, an individual is recognized and identified. In the second case, an unknown individual is present. The last two cases indicate that the image is not a face image.

EIGEN FACE RECOGNITION: 

EIGEN FACE RECOGNITION Collect a set of characteristic face images of the known individuals. Calculate the matrix L, find its eigenvalues and eigenvectors, and choose the M' eigenvectors with the highest associated eigenvalues. Combine the normalized set of images according to produce the (M' = 10) E igen faces k . For each known individual, calculate the class vector k by averaging pattern vector . Choose a threshold that defines the maximum allowable distance from any face class, and a threshold that defines the maximum allowable distance from face space.

EIGEN FACE RECOGNITION: 

EIGEN FACE RECOGNITION For each new image to be identified, calculate its pattern vector , the distance to each known class, and the distance to face space. If the minimum distance < and the distance < , classify the input face as the individual associated with class vector k . If the minimum distance = but distance < , then the image may be classify as "unknown ", and optionally used to begin a new face class. If new image is classified as a known individual, this image may be added to the original set of familiar face images, and the Eigen faces may be recalculated . This gives the opportunity to modify the face space as the system encounters more instances of the known faces

IMPLEMENTATION: 

IMPLEMENTATION The entire sequence of training and testing is sequential and can be broadly classified as consisting of following two steps: 1. Database Preparation 2. Training 3. Testing

Sequence of Implementation: 

S equence of Implementation

TRAINING: 

TRAINING

TESTING: 

TESTING

EXPERIMENTAL RESULTS: 

EXPERIMENTAL RESULTS

EIGEN FACES: 

EIGEN FACES

MEAN IMAGES: 

MEAN IMAGES

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Output : Mohd .. Ubeadulla Age:21

Slide 18: 

Output: L.Rajasekharreddy Age :21

Slide 19: 

Output: Non facial image as input

LIMITATIONS OF PCA: 

LIMITATIONS OF PCA • Facial size normalization • Non-frontal view of the face (3D pose, head movement) • Tolerance to facial expression / appearance (including facial hair & specs) • Invariance to lighting conditions (including indoor / outdoor) • Facial occlusion (sunglasses, hat, scarf, etc.) • Invariance to aging. We tried to minimize the data variations by capturing the facial image subjected to the environment – • Frontal view geometry and • Controlled lighting.

FUTURE SCOPE: 

FUTURE SCOPE This face recognition project is based on Eigen face approach that gives an accuracy maximum of about 92.5%. There is scope for future betterment of the algorithm by using Neural Network technique that can give better results as compared to Eigen face approach. With the help of neural network technique accuracy can be improved . Instead of having a constant threshold, it could be made adaptive, depending upon the conditions and the database available, so as to maximize the accuracy . The whole software is dependent on the database and the database is dependent on resolution of camera . So if good resolution digital camera or good resolution analog camera is used , the results could be considerably improved.

REFERENCES: 

REFERENCES 1. Matthew Turk and Alex Pentland vision and Modeling Group , 2.The Media Laboratory , Massachusetts institute of Technology.Fernando L. Podio and Jeffrey S. Dunn2 3. Matthew Turk IEICE Trans Dec 2001 4. Stan Z.Li & Anil K. Jain “ ” Springer publications 5. http://www.face-rec.org 6. http://www.alglib.net 7. http://math.fullerton.edu/mathews/n2003/JacobiMethodProg.html 8. J. J. Hopfield, "Neural networks and physical systems with emergent collective computation abilities," Proc. Nat. Academy Sci., USA, Vo1.81, 1984, pp.3088-92. 9. Y. Liu and H. Ma, "Pattern recognition using o-orbit finite automata," K. H. Tzao , Editor , Proc. SPIE 1606, Boston, MA, Nov. 1991, pp.226-240. 10. D. H. Ackley, G. E. Hinton, and T. J. Sejnowski , "A learning algorithm for Boltzmann machine," Cognitive Science, Vol. 9, 1985, pp.147-169.

Slide 23: 

THANK YOU