Topic
Eigenface
About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.
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Proceedings Article•
01 Jan 1999
TL;DR: Through numerical simulations and computational complexity evaluations, it is shown the {APEX algorithms exhibit superior capability and interesting features.
Abstract: We present a comparison of three neural PCA techniques: the GHA by Sanger, the APEX by Kung and Diamataras, and the { APEX rst proposed by the present authors. Through numerical simulations and computational complexity evaluations we show the {APEX algorithms exhibit superior capability and interesting features.
8 citations
11 Jun 2014
TL;DR: A facial recognition system, which carry out the classification process by analyzing 3D information of the face using light structured projection and the phase shifting technique, which shows robustness in the recognition process in the presence of gestures and facial expressions.
Abstract: In this paper, a facial recognition system is described, which carry out the classification process by analyzing 3D information of the face. The process begins with the acquisition of the 3D face using light structured projection and the phase shifting technique. The faces are aligned respect a face profile and the region of front, eyes and nose is segmented. The descriptors are obtained using the eigenfaces approach and the classification is performed by linear discriminant analysis. The main contributions of this work are: a) the application of techniques of structured light projection for the calculation of the cloud of points related to the face, b) the use of the phase of the signal to perform recognition with 97% reliability, c) the use of the profile of the face in the alignment process and d) the robustness in the recognition process in the presence of gestures and facial expressions.
8 citations
01 Jan 2001
TL;DR: A new simple and fast algorithm was used to train RNN for calculation principal components from a large set of face images and the ability of the RNN to reconstruct face images is explored.
Abstract: In this paper we describe our study of the recirculation neural network (RNN) for calculation principal components from a large set of face images. A new simple and fast algorithm was used to train RNN. Obtained principal components are used for face recognition. We also explore the ability of the RNN to reconstruct face images. Advantages of the proposed approach are shown.
8 citations
02 Sep 2009
TL;DR: This work develops database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins, and develops a novel criterion to extract features used to partition the identity database.
Abstract: We propose, in this paper, a new biometric identification approach which aims to improve recognition performances in identification systems. We aim to split the identity database into well separated partitions in order to simplify the identification task. In this paper we develop a face identification system and we use the reference algorithms of Eigenfaces and Fisherfaces in order to extract different features describing each identity. These features, which describe faces, are generally optimized to establish the required identity in a classical identification process. In this work, we develop a novel criterion to extract features used to partition the identity database. We develop database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins. Pruning the most dissimilar bins from the query identity features allows us to improve the identification performances. We report results from the XM2VTS database.
8 citations
TL;DR: A method using only genetic algorithms to perform face recognition and comparison in the PCA method obtains higher accurate rates and less processing time.
Abstract: Face recognition methods are computationally very expensive and use too much memory and processing time. An example of a method that allocates many computer resources is the Principal Component Analysis (PCA). In order to reduce processing time, was developed in this paper a method using only genetic algorithms to perform face recognition and comparison in the PCA method obtains higher accurate rates and less processing time.
8 citations