A Symmetric Transformation for LDA-based Face Verification

One of the major problem in face verification is to deal with a few number of images per person to train the system. A solution to that problem is to generate virtual samples from an unique image by doing simple geometric transformations such as translation, scale, rotation and vertical mirroring. In this paper, we propose to use a symmetric transformation to generate a new virtual sample. This symmetric virtual sample is obtained by computing the average between the original image and the vertical mirrored image. The face verification system is based on LDA feature extraction, successfully used in previous studies, and MLP for classification. Experiments were carried out on a difficult multi-modal data\-base, namely BANCA. Results on this database show that our face verification system performs better that the state-of-the-art and also that the addition of the symmetric virtual sample improves the performance

Improving Face Verification using Symmetric Transformation

One of the major problem in face verification is to deal with a few number of images per person to train the system. A solution to that problem is to generate virtual samples from an unique image by doing simple geometric transformations such as translation, scale, rotation and vertical mirroring. In this paper, we propose to use a symmetric transformation to generate a new virtual sample. This symmetric virtual sample is obtained by computing the average between the original image and the vertical mirrored image. The face verification system is based on LDA feature extraction, successfully used in previous studies, and MLP for classification. Experiments were carried out on a difficult multi-modal data\-base, namely BANCA. Results on this database show that our face verification system performs better that the state-of-the-art and also that the addition of the symmetric virtual sample improves the performance

Binary Biometric Representation through Pairwise Adaptive Phase Quantization

Extracting binary strings from real-valued biometric templates is a fundamental step in template compression and protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch, and helper data systems. Quantization and coding is the straightforward way to extract binary representations from arbitrary real-valued biometric modalities. In this paper, we propose a pairwise adaptive phase quantization (APQ) method, together with a long-short (LS) pairing strategy, which aims to maximize the overall detection rate. Experimental results on the FVC2000 fingerprint and the FRGC face database show reasonably good verification performances.\ud \u

3-D Face Analysis and Identification Based on Statistical Shape Modelling

This paper presents an effective method of statistical shape representation for automatic face analysis and identification in 3-D. The method combines statistical shape modelling techniques and the non-rigid deformation matching scheme. This work is distinguished by three key contributions. The first is the introduction of a new 3-D shape registration method using hierarchical landmark detection and multilevel B-spline warping technique, which allows accurate dense correspondence search for statistical model construction. The second is the shape representation approach, based on Laplacian Eigenmap, which provides a nonlinear submanifold that links underlying structure of facial data. The third contribution is a hybrid method for matching the statistical model and test dataset which controls the levels of the model’s deformation at different matching stages and so increases chance of the successful matching. The proposed method is tested on the public database, BU-3DFE. Results indicate that it can achieve extremely high verification rates in a series of tests, thus providing real-world practicality

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers

Setting a world record in 3D face recognition

Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition, iris and face recognition. A relatively new field is 3D face recognition based on the shape of the face rather that its appearance. This paper presents a method for 3D face recognition developed at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente and published in 2011. The paper also shows that noteworthy performance gains can be obtained by optimisation of an existing method. The method is based on registration to an intrinsic coordinate system using the vertical symmetry plane of the head, the tip of the nose and the slope of the nose bridge. For feature extraction and classification multiple regional PCA-LDA-likelihood ratio based classifiers are fused using a fixed FAR voting strategy. We present solutions for correction of motion artifacts in 3D scans, improved registration and improved training of the used PCA-LDA classifier using automatic outlier removal. These result in a notable improvement of the recognition rates. The all vs all verification rate for the FRGC v2 dataset jumps to 99.3% and the identification rate for the all vs first to 99.4%. Both are to our knowledge the best results ever obtained for these benchmarks by a fairly large margin