This paper introduces a new concept of LLE eigenface modelled by local linear embedding (LLE), and compares it with the traditional PCA eigenface from principle component analysis (PCA) on pose identity and face identity recognition through face classification. LLE eigenface is found outperforming PCA eigenface on the discrimination/recogntion of both face identity and pose identity. The superi...

The state-of-the-art in human face recognition is the subspace methods originated by the Principal Component Analysis (PCA), the Eigenfaces of the facial images. Recently, a technique called Two-Dimensional PCA (2DPCA) was proposed for human face representation and recognition. It was developed for image feature extraction based on 2D matrices as opposed to the standard PCA, which is based on 1...

In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedding and image based projection. The 2D Laplacianfaces method is not only computationally more efficient but also more accurate than the one-dimensional (1D) Laplacianfaces method in extracting the facial features for h...

In this paper, a novel algorithm for feature extraction, named supervised kernel locally principle component analysis (SKLPCA), is proposed. The SKLPCA is a non-linear and supervised subspace learning method, which maps the data into a potentially much higher dimension feature space by kernel trick and preserves the geometric structure of data according to prior class-label information. SKLPCA ...

In this paper we evaluate the effectiveness of two likelihood normalization techniques, the Background Model Set (BMS) and the Universal Background Model (UBM), for improving performance and robustness of four face authentication systems utilizing a Gaussian Mixture Model (GMM) classifier. The systems differ in the feature extraction method used: eigenfaces (PCA), 2-D DCT, 2-D Gabor wavelets an...

In this paper, we proposed a new fast technique of face authentication based on extraction of a simple Statistical features derived from the image of face. Once the feature vector is extracted, we comparing it with the feature vector of face which is authenticated, and we calculated the error rates in the two sets of validation and test for the data base XM2VTS according to the protocol of Laus...

In this work we describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In a...

Face recognition is a system that identifies human faces through complex computational techniques. The paper explains two different algorithms for feature extraction. These are Principal Component Analysis and Fisher Faces algorithm. It then explains how images can be recognized using a backpropagation algorithm on a feed forward neural network. Two training databases one containing 20 images a...

The task of gender and age classification based on texts and facial images has been studied extensively in the natural language processing community and the computer vision community respectively. Machine learning algorithms are particularly relevant in solving such classification problems. In the final project for CIS520 Machine Learning, we are presented with the task of gender and age classi...

Color has plenty of discriminative information that can be used to improve the performance of face recognition algorithms, although it is difficult to use it because of its high variability. In this paper we investigate the use of the quaternion representation of a color image for face recognition. We also propose a new representation for color images based on complex numbers. These two color r...