We compare the generalization performance of three distinct representation schemes for facial emotions using a single classification strategy (neural network). The face images presented to the classifiers are represented as: full face projections of the dataset onto their eigenvectors (eigenfaces); a similar projection constrained to eye and mouth areas (eigenfeatures); and finally a projection...

PCA is a well-know dimension reduction methodology that can also be employed for face detection task. Although the use of eigenface as the basis for face detection or recognition under PCA-based regime is so generalized, little is known about its characteristics. We study its feature by data visualization in the face space of varying dimension and the comparison of the face detection rate in di...

In order to reduce the impact of block for the rate of face recognition ,in this paper, through the control of sparseness in the non-negative matrix factorization , the face image do non-negative sparse coding to obtain the eigenspace for the image. The experiment uses the ORL face database. The experimental results show that using NMFs obtains Eigenfaces with the local features of face and has...

This paper is focused on algorithmic issues for biometric face verification (i.e., given an image of the face and an identity claim, decide whether they correspond to each other or not). Several alternatives for geometric normalization of images, photometric normalization, dimensionality reduction and similarity measures are proposed and compared using the XM2VTS database and the associated Lau...

In this paper we investigate the face recognition problem via the overlapping energy histogram of the DCT coefficients. Particularly, we investigate some important issues relating to the recognition performance, such as the issue of selecting threshold and the number of bins. These selection methods utilise information obtained from the training dataset. Experimentation is conducted on the Yale...

Human face recognition is one of the most intensely researched areas of machine vision. Many machine vision algorithms have been adapted or developed specifically for the task of face recognition. By comparison, very little research has been done to develop systems for the recognition of other classes of objects. This paper will review a selection of the most popular face recognition algorithms...

Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [1]. In this paper we propose a new approach to detect the human faces quickly and efficiently by using Eigen faces technique along with ANN using resilient back propagation algorithm. In this process the face structure can be converted into Eigen values. T...

Este artigo apresenta uma nova metodologia para o reconhecimento de faces, um importante e difícil problema que tem sido estudado pela comunidade de visão computacional e reconhecimento de padrões. A metodologia utilizada modela a imagem de uma face através de uma rede complexa e medidas são extraídas sobre essas redes para a composição do vetor de característica. Os experimentos foram conduzid...

A critical issue in an automatic face recognition system is the determination of the region containing a face in an image with a cluttered background. This paper presents a new method that optimizes the detection task through the use of Eigenfaces, neural networks and a bootstrap algorithm. The main component of the proposed method is a non-linear operator that detects the presence of a wellfra...

We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of t...