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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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Journal ArticleDOI
TL;DR: A comprehensive comparative analysis of the performance of locality preserving projections (LPPs)‐ based Laplacianfaces, which is a recently introduced algorithm with the more traditional, principal component analysis (PCA)‐based Eigenfaces, to provide best combination of selected parameters to extract the best results.
Abstract: This paper provides a comprehensive comparative analysis of the performance of locality preserving projections (LPPs)‐based Laplacianfaces, which is a recently introduced algorithm with the more traditional, principal component analysis (PCA)‐based Eigenfaces All possible combinations of neighbourhood defining distance metrics, classifier distance metrics and number of retained eigenvectors have been tried on different imaging environments The FERET facial database was chosen which provides enough diversity in illumination, facial expressions and aging CsuFaceIdEval, an open source platform, is used for this comparison and recognition rates are studied in detail As a result of our detailed analysis, we provide best combination of selected parameters to extract the best results from these two algorithms

6 citations

Proceedings ArticleDOI
01 Dec 2015
TL;DR: A simple and effective method that can be integrated into any face and expression recognition system to improve the overall recognition accuracy even under limitation of training samples is proposed.
Abstract: Although important and effective contributions on face recognition under varying facial expressions have been reported up to date, most of the methods need multiple images of an individual stored in the database. However, this problem becomes more challenging when a limited number of training samples are available as is the case for expression invariant face identification for surveillance and security applications. This paper proposes a simple and effective method that can be integrated into any face and expression recognition system to improve the overall recognition accuracy even under limitation of training samples. In this approach, neutral component of the expressive image is estimated utilizing prior information obtained from different subjects under the same expression. Basically by analyzing the impact of a particular expression on a neutral face a nullification process is developed to convert the expressive image to a neutral face. In order to make it justifiable to utilize common expression information for different subjects, an alignment strategy is employed where for each expression a specific expression template is used, and the images are warped to their corresponding expression face template. After negating the facial expression from the expressive images, principal component analysis (PCA) is applied to reduce the dimension and cosine similarity matching is used for classification. The experimental results on Cohn-Kanade database exhibit the effectiveness of the proposed method even when there is a single training sample per class is available in the database.

6 citations

Proceedings ArticleDOI
01 Aug 2014
TL;DR: A CUDA implementation of eigenface approach for face recognition is presented and the proposed algorithm has shown a 5× speedup in training phase.
Abstract: Face recognition has many real world applications including surveillance and authentication. Due to complex and multidimensional structure of face it requires huge computations therefore fast face recognition is required. One of the most successful appearance based techniques for face recognition is Principal Component Analysis (PCA) which is generally known as eigenface approach. It suffers from the disadvantage of higher computation cost, despite its better recognition rate. With the increase in number of images in training database and also the resolution of images, the computational cost also increases. In this paper, we present a CUDA implementation of eigenface approach for face recognition. The proposed algorithm has shown a 5× speedup in training phase.

6 citations

Proceedings ArticleDOI
19 May 2011
TL;DR: An audio-video system for intelligent environments with the capability to recognize people is presented, implemented using consumer electronics and scalable in the number of cameras and microphones thanks to NMM, a middleware software which manages the processing of the single sensors and the communications among the several software nodes.
Abstract: In this paper an audio-video system for intelligent environments with the capability to recognize people is presented. Users are tracked inside the environment and their positions and activities can be logged. Users identities are assessed through a multimodal approach by detecting and recognizing voices and faces through the different cameras and microphones installed in the environment. This approach has been chosen in order to create a flexible and cheap but reliable system, implemented using consumer electronics. Voice features are extracted by a short time cepstrum analysis, and face features are extracted using the eigenfaces technique. The recognition task is solved using the same Support Vector Machine for both voice and face features. The system learns the features of each person using SVM in a set-up phase, in which the two modalities are also bound together through a cross-anchoring learning rule based on the mutual exclusivity selection principle. In the running phase the system is able to recognize the identity of the person either using voice features, or face features or both. The system is scalable in the number of cameras and microphones thanks to NMM, a middleware software which manages the processing of the single sensors and the communications among the several software nodes.

6 citations

Proceedings ArticleDOI
23 Jun 2017
TL;DR: Propose method which is used will detect occluded face and recognize the face with the help of given same faces from the database, and other appropriate methods are Principal Component Analysis (PCA), Local Binary Pattern (LBP), Eigenfaces.
Abstract: Face recognition is one of the most important problems of verifying or identifying a face from query image or input image. This system has emerged as an important field in case of surveillance systems. Face detection is a very powerful tool for video surveillance, human computer interface, face recognition, and image database management. Occlusion means extraneous objects that hinder face recognition, e.g., face covered with scarf, wearing glasses, beard, cap etc., is one of the greatest challenges in face recognition systems. Other issues are illumination, pose, expressions etc. An efficient method is used for detection of occlusions, which specifies the missing information in the occluded face. Method used for face detection is Viola-Jones algorithm, for occlusion detection and reconstruction of face fast weighted PCA is used are Neural Network (NN) is used for face recognition. Other appropriate methods are Principal Component Analysis (PCA), Local Binary Pattern (LBP), Eigenfaces. Propose method which is used will detect occluded face and recognize the face with the help of given same faces from the database.

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
202249
202120
202043
201953
201840