Face Recognition Using Eigen Faces and Artificial Neural Network

Abstract

Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages-Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave a recognition rate of 97.018%

Comparison of Different Neural Network Training Algorithms with Application to Face Recognition

“…Whereas reflectance and shape are elemental properties of a face object, the appearance of a face is also subject to several other factors, including the facial expression and facial pose. In addition to these, various imaging parameters, such as exposure time, aperture, lens aberrations, and sensor spectral response also increase subject variations [11]. Nevertheless, the number of examples per person, that are available for learning, is often much smaller than the dimensionality of the image space.…”

“…Nevertheless, the number of examples per person, that are available for learning, is often much smaller than the dimensionality of the image space. A system that was trained using so few examples may not work well to unseen instances of the face [11].…”

“…In practice, the training set of face images is rather small, and that makes calculations manageable. However, since the algorithm itself assumes that the problem is linear, authors [11] indicate that the results obtained are not satisfying. Therefore, we introduced neural networks which are used for nonlinear problems.…”

Feature Level Fusion of Multimodal Biometrics and Two Tier Security in ATM System

“…Two basic methods are involved in face recognition. The first method is based on extracting feature vectors from the basic parts of a face such as eyes, nose, mouth and chin with the help of deformable templates [18]. The key information id got from the basic parts of face and it is gathered and converted into a feature vector.…”

Efficient Face Recognition System using Artificial Neural Network

“…Automatic face recognition can be done using different combination of feature extraction methods and classifiers. In many cases features are extracted from whole facial space using Principal Component Analysis (PCA) [2] [3]. On other hand it involves extraction of features from face components [4] [5].…”