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Proceedings ArticleDOI

A generic face representation approach for local appearance based face verification

TLDR
The experimental results show that the proposed local appearance based approach provides better and more stable results than the baseline system -holistic Eigenfaces- approach.
Abstract
In this paper we present the experimental results of a generic local appearance based face representation approach obtained from the first and fourth experiments of the Face Recognition Grand Challenge (FRGC) version 1 data. The introduced representation approach is compared with the baseline system with the standard distance metrics of L1 norm, L2 norm and cosine angle. The experimental results show that the proposed local appearance based approach provides better and more stable results than the baseline system -holistic Eigenfaces- approach.

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Citations
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Journal ArticleDOI

Enabling Multimodal Human–Robot Interaction for the Karlsruhe Humanoid Robot

TL;DR: The systems for spontaneous speech recognition, multimodal dialogue processing, and visual perception of a user, which includes localization, tracking, and identification of the user, recognition of pointing gestures, as well as the recognition of a person's head orientation are presented.
Proceedings ArticleDOI

Analysis of Local Appearance-Based Face Recognition: Effects of Feature Selection and Feature Normalization

TL;DR: The experimental results show the importance of using appropriate feature sets and doing normalization on the feature vector and the effects of feature selection and feature normalization to the performance of a local appearance based face recognition scheme.
Journal ArticleDOI

A Survey on Perception Methods for Human–Robot Interaction in Social Robots

TL;DR: This paper reviews several widely used perception methods of HRI in social robots and investigates general perception tasks crucial for HRI, such as where the objects are located in the rooms, what objects are in the scene, and how they interact with humans.
Proceedings ArticleDOI

Audio-visual multi-person tracking and identification for smart environments

TL;DR: A novel system for the automatic and unobtrusive tracking and identification of multiple persons in an indoor environment using a particle filter framework to simultaneously track multiple occupants and showed good performance in natural meeting scenarios at quickly acquiring their identities and complementing the ID information missing in single modalities.
Journal ArticleDOI

Local appearance based face recognition method using block based steerable pyramid transform

TL;DR: Experimental results on ORL, Yale, Essex and FERET face databases convince us that the proposed S-P method provides a better representation of the class information, and obtains much higher recognition accuracies in real-world situations including changes in pose, expression and illumination.
References
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Journal ArticleDOI

Eigenfaces for recognition

TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI

Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI

Face recognition: features versus templates

TL;DR: Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching are presented.
Proceedings ArticleDOI

View-based and modular eigenspaces for face recognition

TL;DR: In this paper, a view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose, which incorporates salient features such as the eyes, nose and mouth, in an eigen feature layer.
Journal ArticleDOI

Face recognition by independent component analysis

TL;DR: Independent component analysis (ICA), a generalization of PCA, was used, using a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons, which was superior to representations based on PCA for recognizing faces across days and changes in expression.