Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Fusion levels of visible and infrared modalities for face recognition

Abstract: We present a study on different levels of visible and infrared modalities fusion for face recognition. While visible modality is the most natural way to recognize someone, infrared presents thermal distribution that can be useful for face recognition. We compare the well-known eigenfaces method as a baseline to an approach based on sparsity for the feature extraction and the classification. Applied on the Notre-Dame database, we showed that the three levels of fusion considered are not equivalent in term of final identification rates. We also show that the sparse approach at the decision level outperforms the state-of-art on this database.

Linear Discriminant Analysis for 3D Face Recognition Using Radon Transform

Abstract: Face recognition research started in the 70s and a number of algorithms/systems have been developed in the last decade. Three Dimensional (3D) human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. Three Dimensional face recognition also helps to resolve some of the issues associated with two dimensional (2D) face recognition. Since 2D systems employ intensity images, their performance is reported to degrade significantly with variations in facial pose and ambient illumination. The 3D face recognition systems, on the other hand, have been reported to be less sensitive to the changes in the ambient illumination during image capture that the 2D systems. In the previous works, there are several methods for face recognition using range images that are limited to the data acquisition and preprocessing stage only. In the present paper, we have proposed a 3D face recognition algorithm which is based on Radon transform, principal component analysis (PCA) and linear discriminant analysis (LDA). The radon transform (RT) is a fundamental tool to normalize 3D range data. The PCA is used to reduce the dimensionality of feature space, and the LDA is used to optimize the features, which are finally used to recognize the faces. The experimentation has been done using Texas 3D face database. The experimental results show that the proposed algorithm is efficient in terms of accuracy and detection time, in comparison with other methods based on PCA only and RT + PCA. It is observed that 40 eigenfaces of PCA and 5 LDA components lead to an average recognition rate of 99.16 %.

Random Projection with Robust Linear Discriminant Analysis Model in Face Recognition

Abstract: This paper presents a face recognition technique with two techniques: random projection (RP) and robust linear discriminant analysis model (RDM). RDM is an enhanced version of fisher's linear discriminant with energy-adaptive regularization criteria. It is able to yield better discrimination performance. Same as Fisher's Linear Discriminant, it also faces the singularity problem of within-class scatter. Thus, a dimensionality reduction technique, such as principal component analysis (PCA), is needed to deal with this problem. In this paper, RP is used as an alternative to PCA in RDM in the application of face recognition. Unlike PCA, RP is training data independent and the random subspace computation is relatively simple. The experimental results illustrate that the proposed algorithm is able to attain better recognition performance (error rate is approximately 5% lower) compared to Fisherfaces.

Robust Face Recognition from a Single Training Image per Person with Kernel-Based SOM-Face

Abstract: In this paper, a kernel-based SOM-face method is proposed to recognize expression variant faces under the situation of only one training image per person. Based on the localization of the face, an unsupervised kernel-SOM learning procedure is carried out to capture the common local features and the non-Euclidean structure of the image data, so that a compact and robust representation of the face can be obtained. Experiments on the FERET face database show that the Kernel-based SOM-face method can obtain higher recognition performance than the regular SOM-face method.

Research on Face Recognition Based on PCA

Abstract: Studies the face recognition,applyies the images training algorithm based on principal component analysis to face recognition and uses the nearest neighbor classifier such as Euclidean distance to category all the training face images,and then designes a set of face recognition attendance system based on PCA,which have applied to the practice of the company's attendance.The actual result indicates that using the PCA algorithm for face recognition can make the recognition rate achieve more than 90%.