Feature extraction and discriminating feature selection for 3D face recognition
23 Oct 2009-pp 44-49
TL;DR: The results show that the most energetic features, low frequency components, are not the most discriminating features in this 3D face recognition method.
Abstract: This paper presents a 3D face recognition method In this method, 3D Discrete Cosine Transform (DCT) is used to extract features Before the feature extraction, faces are aligned with respect to nose tip and then registered two times: according to average nose and average face Then the coefficients of 3D transformation are calculated The most discriminating 3D transform coefficients are selected as the feature vector where the ratio of between-class variance and within-class variance is used for discriminant coefficient selection The results show that the most energetic features, low frequency components, are not the most discriminating features The method was also modified based on 3D Discrete Fourier Transform (DFT) for feature selection as regarding real and complex DFT coefficients as independent features Discriminating features were matched by using the Nearest Neighbor classifier Recognition experiments were realized on 3D RMA face database The proposed method yileds a recognition rate above 99% for 3D DCT based features
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TL;DR: This work provides the first guideline for supporting the development of an automatic face recognition approach by analysing strengths and constraints of what is available in the geometrical domain by the use of a set of indicators.
33 citations
TL;DR: This paper addresses the problem of real-time and low-power 3D DCT/IDCT processing by presenting a context-aware fast transform algorithm and a family of VLSI architectures characterized by different levels of parallelism.
Abstract: The 3D discrete cosine transform and its inverse (3D DCT/IDCT) extend the spatial compression properties of conventional 2D DCT to the spatio-temporal coding of 2D videos The 3D DCT/IDCT transform is particularly suited for embedded systems needing the low-complexity implementation of both video encoder and decoder, such as mobile terminals with video-communication capabilities This paper addresses the problem of real-time and low-power 3D DCT/IDCT processing by presenting a context-aware fast transform algorithm and a family of VLSI architectures characterized by different levels of parallelism Implemented in submicron CMOS technology, the proposed hardware macrocells support the real-time processing of main video formats (up to high definition ones with an input rate of tens of Mpixels/s) with different trade-offs between circuit complexity, power consumption and computational throughput Voltage scaling and adaptive clock-gating strategies are applied to reduce the power consumption versus the state of the art
25 citations
TL;DR: This study investigates the use of a 3D discrete cosine transform (DCT) for 3D face recognition and presents a novel 3D DCT-based feature extraction method with the selection of discriminating coefficients, showing that a hybrid feature selection method has the best performance both in terms of time and recognition.
Abstract: In this study, we investigate the use of a 3D discrete cosine transform (DCT) for 3D face recognition and present a novel 3D DCT-based feature extraction method with the selection of discriminating coefficients. We apply a 3D DCT on the voxel data, and use transform coefficients as features. Then the most discriminating 3D transform coefficients are selected with the proportion of variance, sequential floating forward selection and sequential floating backward selection methods. After feature selection, the linear discriminant analysis is applied on reduced sized feature vectors. We compare the results of different feature selection methods and show that a hybrid feature selection method has the best performance both in terms of time and recognition. Our experimental results verify that the discriminating DCT coefficients increase the face recognition rate more than the low-indexed coefficients do. On the other hand, the discriminating coefficients have only an energy level of 1.58%, too low when compared with the total energy of low-indexed coefficients. This fact shows that the discriminating coefficients are not the most energetic ones. With these coefficients, a recognition rate of 99.25% is achieved and this result is compared with other methods tested on a 3D RMA face database.
14 citations
Cites background from "Feature extraction and discriminati..."
...In our calculations, the dimension of the feature vector is practically selected as lower than the number of training samples [36]....
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29 Nov 2010
TL;DR: A new algorithm which utilizes the combination of texture and depth information to overcome the problem of pose variation and illumination change for face recognition and shows considerable enhancement compared to previous methods.
Abstract: The efficiency of a human face recognition system depends on the capability of face recognition in presence of different changes in the appearance of face. One of the main difficulties regarding the face recognition systems is to recognize face in different views and poses. In this paper we propose a new algorithm which utilizes the combination of texture and depth information to overcome the problem of pose variation and illumination change for face recognition. In the proposed algorithm, we first use intensity image to extract efficient key features and find probable face matches in the face database using feature matching algorithm. We have defined some criteria to find the final match based on texture information or leave the decision to second stage. In the second stage the depth information are normalized and used for pose invariant face recognition. We tested the proposed algorithm using a face database with different poses and illumination and compared the results with those of other methods. We obtained the recognition rate of 88.96 percent which shows the considerable enhancement compared to previous methods.
13 citations
01 Jan 2020
TL;DR: This paper addresses the 3D face recognition problem in order to select the most meaningful geometric descriptors with the results state the importance of the curvedness novel descriptors and only of a few Euclidean distances.
Abstract: In pattern recognition, neural networks can be used not only for the classification task, but also for feature selection and other intermediate steps. This paper addresses the 3D face recognition problem in order to select the most meaningful geometric descriptors. At this aim, the classification results are directly integrated in a biclustering process in order to select the best leaves of a neural hierarchical tree. This tree is created by a novel neural network GH-EXIN. This approach results in a new criterion for the feature selection. This technique is applied to a database of face expressions where both traditional and novel geometric descriptors are used. The results state the importance of the curvedness novel descriptors and only of a few Euclidean distances.
8 citations
References
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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.
Abstract: The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is 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. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >
17,598 citations
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.
6,384 citations
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Abstract: Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques.
In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Contact: yvan.saeys@psb.ugent.be
Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview
4,706 citations
"Feature extraction and discriminati..." refers background in this paper
...However, since its rigid registration, performance degrades with the expression variation....
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20 Jun 2005
TL;DR: The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images.
Abstract: Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.
2,595 citations
"Feature extraction and discriminati..." refers methods in this paper
...978-1-4244-5023-7/09/$25.00 ©2009 IEEE September 14-16, 2009 METU Northern Cyprus Campus44 In this method, 3D Discrete Cosine Transform (DCT) is used to extract features....
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TL;DR: Independent component analysis (ICA), a generalization of PCA, was used, using a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons, which was superior to representations based on PCA for recognizing faces across days and changes in expression.
Abstract: A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.
2,044 citations