Eigenface

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

Discriminative Covariance Oriented Representation Learning for Face Recognition with Image Sets

Abstract: For face recognition with image sets, while most existing works mainly focus on building robust set models with hand-crafted feature, it remains a research gap to learn better image representations which can closely match the subsequent image set modeling and classification. Taking sample covariance matrix as set model in the light of its recent promising success, we present a Discriminative Covariance oriented Representation Learning (DCRL) framework to bridge the above gap. The framework constructs a feature learning network (e.g. a CNN) to project the face images into a target representation space, and the network is trained towards the goal that the set covariance matrix calculated in the target space has maximum discriminative ability. To encode the discriminative ability of set covariance matrices, we elaborately design two different loss functions, which respectively lead to two different representation learning schemes, i.e., the Graph Embedding scheme and the Softmax Regression scheme. Both schemes optimize the whole network containing both image representation mapping and set model classification in a joint learning manner. The proposed method is extensively validated on three challenging and large scale databases for the task of face recognition with image sets, i.e., YouTube Celebrities, YouTube Face DB and Point-and-Shoot Challenge.

Making saturated facial images useful again

Abstract: Saturation here refers to electronic saturation of the camera sensors which produces clipped colors, and not the purity of color as in the hue-saturation and value scale. Saturated images are routinely discarded in image analysis yet there are situations when they cannot be avoided. This paper proposes two strategies to recover color information in facial images taken under non-ideal conditions to make them useful for further processing. The first assumes that the skin is matte and that there are parts of the image which are not clipped. Ratios between R, G and B values of unclipped pixels belonging to the same parts of the image may then be used to compute for lost channel values. The second approach uses color eigenfaces computed from our physics-based face database obtained under different illuminants and camera calibration conditions. Skin color is recovered by transforming the first few eigenface coefficients towards ideal condition values. Excellent color recovery for clipped images is achieved when these two techniques are combined and used on face images captured under daylight illuminant with a camera white balanced for incandescent light.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Appearance-based statistical methods for face recognition

Abstract: Different statistical methods for face recognition have been proposed in recent years. They mostly differ in the type of projection and distance measure used. The aim of this paper is to give an overview of most popular statistical subspace methods for face recognition task. Theoretical aspects of three algorithms will be considered and some reported performance evaluations will be given.