Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition, and tracking. The representation in these subspace methods is based on second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics and In...

This paper presents a face recognition system employing eigenface-based approach. The principal objective of this research is to extract feature vectors from images and to reduce the dimension of information. The method is implemented on frontal view facial images of persons to explore a twodimensional representation of facial images. The system is organized with RMS (Root Mean Square) contrast...

Abstract. Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face recognition system, which has the ability to extract local features from an...

This paper motivates and describes a novel realization of transductive inference that can address the Open Set face recognition task. Open Set operates under the assumption that not all the test probes have mates in the gallery. It either detects the presence of some biometric signature within the gallery and finds its identity or rejects it, i.e., it provides for the " none of the above " answ...

Face recognition systems can normally attain good accuracy when they are provided with a large set of training examples. However, when a large training set is not available, their performance is commonly poor. In this work we describe a method for face recognition that achieves good results when only a very small training set is available (it can work with a training set as small as one image p...

Face recognition is an active area of biometrics. This study investigates the use of Chain Codes as features for recognition purpose. Firstly a segmentation method, based on skin color model was applied, followed by contour detection, then the chain codes of the contours were determined. The first difference of chain codes were calculated since the latter is invariant to rotation. The features ...

These approaches utilize different features for face recognition purpose. The feature utilized for face recognition are shape, distance between two traits of face, texture features for face. Texture features are particularly susceptible to the resolution of images, when the resolution changes the calculated textures are not accurate. Texture features computed for low resolution face images does...

In this Bachelor thesis the use of distortion models in context of face recognition is evaluated. Previous work on optical character recognition indicated the usefulness of distortion models for image recognition tasks. On the other hand it has been shown that local appearance-based features such as DCT-features can be used to accommodate for illumination variances. In this thesis, these two ap...

Hidden Markov Models (HMM) have been successfully used for speech and action recognition where the data that is to be modeled is one-dimensional. Although attempts to use these one-dimensional HMMs for face recognition have been moderately successful, images are two-dimensional (2-D). Since 2-D HMM's are too complex for real-time face recognition, in this paper we present a new approach for fac...

An appearance-based face recognition approach called the L-Fisherfaces is proposed in this paper, By using Local Fisher Discriminant Embedding (LFDE), the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information, and obta...