Eigenface

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

Clustering Facial Attributes: Narrowing the Path From Soft to Hard Biometrics

Abstract: Despite the success obtained in face detection and recognition over the last ten years of research, the analysis of facial attributes still represents a trend topic. Keeping the full face recognition aside, exploring the potentials of soft biometric traits, i.e. singular facial traits like the nose, the mouth, the hair and so on, is yet considered a fruitful field of investigation. Being able to infer the identity of an occluded face, e.g. voluntary occluded by sunglasses or accidentally due to environmental factors, can be useful in a wide range of operative fields where user collaboration cannot be considered as an assumption. This especially happens when dealing with forensic scenarios in which is not unusual to have partial face photos or partial fingerprints. In this paper, an unsupervised clustering approach is described. It consists in a neural network model for face attributes recognition based on transfer learning whose goal is grouping faces according to common facial features. Moreover, we use the features collected in each cluster to provide a compact and comprehensive description of the faces belonging to each cluster and deep learning as a mean for task prediction in partially visible faces.

Recognition of expression variant faces using weighted subspaces

Abstract: In the past decade or so, subspace methods have been largely used in face recognition - generally with quite success. Subspace approaches, however, generally assume the training data represents the full spectrum of image variations. Unfortunately, in face recognition applications one usually has an under-represented training set. A known example is that posed by images bearing different expressions; i.e., where the facial expressions in the training image and in the testing image diverge. If the goal is to recognize the identity of the person in the picture, facial expressions are seen as distracters. Subspace methods do not address this problem successfully, because the feature-space learned is dependent over the set of training images available - leading to poor generalization results. In this communication, we show how one can use the deformation of the face (between the training and testing images) to solve the above defined problem. To achieve this, we calculate the facial deformation between the testing and each of the training images, project this result onto the (learned) subspace, and there weight each of the features (dimensions) inverse-proportionally to the estimated deformation. We show experimental results of our approach on those representations given by the following subspace techniques: principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA). We also present comparison results with a number of known techniques and show the superiority of our weighted LDA algorithm over the rest.

A Similarity Measure Based on Hausdorff Distance for Human Face Recognition

Abstract: A similarity measure based on Hausdorff distance (SMBHD) for face recognition is proposed in this paper. Different from the conventional Hausdorff distance based measures, the proposed measure can provide not only the dissimilarity information but also the similarity information of two objects to compare them. The added similarity information can especially better the discriminating capability of an object recognition system for similar objects such as faces with variant lighting condition and facial expression. In order to evaluate the performance of a face recognition system using the proposed similarity measure based on Hausdorff distance (SMBHD), the face images included in the AR, ORL, and Yale face databases have been used. The Experimental results show that the system has a better performance than the systems based on conventional Hausdorff distance measures and the Eigenfaces approaches.

Nonnegative-Least-Square Classifier for Face Recognition

Abstract: In this paper, we propose a novel classification method, based on Nonnegative-Least-Square (NNLS) algorithm, for face recognition Different from traditional classifiers, in our classifier, we consider each new sample (face) as a nonnegative linear combination of training samples (faces) By forcing the nonnegative constraint on linear coefficients, we obtain the nonnegative sparse representation that automatically discriminates between those classes present in the training set Experimental results show the promising aspects of new classifier when comparing with the most popular classifiers such as Nearest Neighborhood (NN), Nearest Centroid (NC), and Nearest Subspace (NS) in terms of recognition accuracy, efficiency, and numerical stability Eigenfaces, Fisherfaces, and Laplacianfaces are performed on Yale and ORL databases as feature extraction in these experiments

More robust face recognition by considering occlusion information

Abstract: This paper addresses one of the main challenges of face recognition (FR): facial occlusions Currently, the human brain is the most robust known FR approach towards partially occluded faces Nevertheless, it is still not clear if humans recognize faces using a holistic or a component-based strategy, or even a combination of both In this paper, three different approaches based on principal component analysis (PCA) are analyzed The first one, a holistic approach, is the well-known eigenface approach The second one, a component-based method, is a variation of the eigenfeatures approach, and finally, the third one, a near-holistic method, is an extension of the lophoscopic principal component analysis (LPCA) So the main contributions of this paper are: The three different strategies are compared and analyzed for identifying partially occluded faces and furthermore it explores how a priori knowledge about present occlusions can be used to improve the recognition performance