Journal ArticleDOI
Automatic 3D face recognition from depth and intensity Gabor features
TLDR
A novel hierarchical selecting scheme embedded in linear discriminant analysis (LDA) and AdaBoost learning is proposed to select the most effective and most robust features and to construct a strong classifier for face recognition systems.About:
This article is published in Pattern Recognition.The article was published on 2009-09-01. It has received 108 citations till now. The article focuses on the topics: Three-dimensional face recognition & Gabor filter.read more
Citations
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Journal ArticleDOI
A feature extraction method for use with bimodal biometrics
Yong Xu,David Zhang,Jingyu Yang +2 more
TL;DR: The experiments show that the proposed matrix-based complex PCA, a feature level fusion method for bimodal biometrics that uses a complex matrix to denote two biometric traits from one subject, can achieve a higher classification accuracy than the 2DPCA and PCA techniques.
Journal ArticleDOI
meshSIFT: Local surface features for 3D face recognition under expression variations and partial data
TL;DR: The meshSIFT algorithm and its use for 3D face recognition is presented, and it is demonstrated that symmetrising the feature descriptors allows comparing two 3D facial surfaces with limited or no overlap.
Journal ArticleDOI
An efficient 3D face recognition approach using local geometrical signatures
TL;DR: This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face which can handle expression variations.
Proceedings ArticleDOI
An RGB-D Database Using Microsoft's Kinect for Windows for Face Detection
TL;DR: An RGB-D database containing 1581 images (and their depth counterparts) taken from 31 persons in 17 different poses and facial expressions using a Kinect device is proposed and used in a face detection algorithm which is based on the depth information of the images.
Journal ArticleDOI
Recent Advances on Singlemodal and Multimodal Face Recognition: A Survey
TL;DR: This paper categorizes these algorithms into singlemodal and multimodal face recognition and evaluates methods within each category via detailed descriptions of representative work and summarizations in tables.
References
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Journal ArticleDOI
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.