Kamran Etemad

Discriminant analysis for recognition of human face images

In this paper the discriminatory power of various human facial features is studied and a new scheme for Automatic Face Recognition (AFR) is proposed. Using Linear Discriminant Analysis (LDA) of different aspects of human faces in spatial domain, we first evaluate the significance of visual information in different parts/features of the face for identifying the human subject. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class and minimizing within-class variations. The result is an efficient projection-based feature extraction and classification scheme for AFR. Soft decisions made based on each of the projections are combined, using probabilistic or evidential approaches to multisource data analysis. For medium-sized databases of human faces, good classification accuracy is achieved using very low-dimensional feature vectors.

Face recognition using discriminant eigenvectors

The discriminatory power of different segments of a human face is studied end a new scheme for face recognition is proposed. We first focus on the linear discriminant analysis (LDA) of human faces in spatial and wavelet domains, which enables us to objectively evaluate the significant of visual information in different parts of the face for identifying the person. The results of this study can be compared with subjective psychovisual findings. The LDA of faces also provides us with a small set of features that carry the most relevant information for face recognition. The features are obtained through the eigenvector analysis of scatter matrices with the objective of maximizing between class variations and minimizing within class variations. The result is an efficient projection based feature extraction and classification scheme for recognition of human faces. For a midsize database of faces excellent classification accuracy is achieved with only four features.

Separability based tree structured local basis selection for texture classification

A new algorithm for task dependent selection of wavelet packet trees for signal classification is suggested. The algorithm is based on a class separability measure rather than energy or entropy. At each level the class separabilities obtained from a parent node and its children are computed and compared. The decomposition of the node (or subband) is performed if it provides larger separability. The suggested algorithm is tested for texture classification. The method can also be used with other tree structured local basis e.g. local trigonometric basis functions. Also it can be applied to detection, classification or segmentation of different l-D and 2-D signals.<<ETX>>

Phoneme recognition based on multi-resolution and non-causal context

An alternative view of neural-network-based phoneme recognition using multiresolution ideas and noncausal context is suggested. Some suggestions are made regarding target and error weight functions to improve performance and simplify training. Based on these observations, a simple network with self recurrent links of different delays is proposed and tested on the task of speaker- independent recognition of unvoiced plosives, (p,t,k), with input feature vectors derived from an auditory model.<<ETX>>