Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results
Present days most of the door lock systems are organized by humans with the application of keys, sec...
Abstract—People are usually identified by their faces. Developments in the past few decades have ena...
The aim of this project is to develop a face recognition system based on Scale-Invariant Feature Tra...
Several pattern recognition and classification techniques have been applied to the biometrics domain...
In this paper we present a face recognition system based on the Scale Invariant Feature Transform (S...
In this paper we present a face recognition system based on the Scale Invariant Feature Transform (S...
This paper presents a face recognition algorithm based on the matching of local features extracted f...
This paper presents a face recognition algorithm based on the matching of local features extracted f...
This paper presents a face recognition algorithm based on the matching of local features extracted f...
Scale Invariant Feature Transform (SIFT) proposed by Lowe has been widely and successfully applied t...
The main goal of this work is to develop a fully automatic face recognition algorithm. Scale Invaria...
Face recognition has been recognized as one of the most promising biometric systems. One challenge i...
Two of the most important state-of-the-art challenges in face recognition are: dealing with image ac...
Two of the most important state-of-the-art challenges in face recognition are: dealing with image ac...
In this paper, face recognition using the most representative SIFT images is presented. It is based ...
Present days most of the door lock systems are organized by humans with the application of keys, sec...
Abstract—People are usually identified by their faces. Developments in the past few decades have ena...
The aim of this project is to develop a face recognition system based on Scale-Invariant Feature Tra...
Several pattern recognition and classification techniques have been applied to the biometrics domain...
In this paper we present a face recognition system based on the Scale Invariant Feature Transform (S...
In this paper we present a face recognition system based on the Scale Invariant Feature Transform (S...
This paper presents a face recognition algorithm based on the matching of local features extracted f...
This paper presents a face recognition algorithm based on the matching of local features extracted f...
This paper presents a face recognition algorithm based on the matching of local features extracted f...
Scale Invariant Feature Transform (SIFT) proposed by Lowe has been widely and successfully applied t...
The main goal of this work is to develop a fully automatic face recognition algorithm. Scale Invaria...
Face recognition has been recognized as one of the most promising biometric systems. One challenge i...
Two of the most important state-of-the-art challenges in face recognition are: dealing with image ac...
Two of the most important state-of-the-art challenges in face recognition are: dealing with image ac...
In this paper, face recognition using the most representative SIFT images is presented. It is based ...
Present days most of the door lock systems are organized by humans with the application of keys, sec...
Abstract—People are usually identified by their faces. Developments in the past few decades have ena...
The aim of this project is to develop a face recognition system based on Scale-Invariant Feature Tra...