다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources-Gabor wavelets and LBP-showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions.

/pdf/enhanced-local-texture-feature-sets-for-face-recognition-4ixve2vsid.pdf

Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

/pdf/local-binary-patterns-and-its-application-to-facial-image-1kaldi7ot7.pdf

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.

/pdf/brief-computing-a-local-binary-descriptor-very-fast-3xikz90qxi.pdf

BRIEF: Computing a Local Binary Descriptor Very Fast

Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.

/pdf/enhanced-local-texture-feature-sets-for-face-recognition-10dq3jnge0.pdf

Enhanced local texture feature sets for face recognition under difficult lighting conditions

One of the major problem in face authentication systems is to deal with variations in illumination. In a realistic scenario, it is very likely that the lighting conditions of the probe image does not correspond to those of the gallery image, hence there is a need to handle such variations. In this work, we present a new preprocessing algorithm based on local binary patterns (LBP): a texture representation is derived from the input face image before being forwarded to the classifier. The efficiency of the proposed approach is empirically demonstrated using both an appearance-based (LDA) and a feature-based (HMM) face authentication systems on two databases: BANCA and XM2VTS (with its darkened set). Conducted experiments show a significant improvement in terms of verification error rates and compare to results obtained with state-of-the-art preprocessing techniques.

/pdf/local-binary-patterns-as-an-image-preprocessing-for-face-58okbebeba.pdf

Local binary patterns as an image preprocessing for face authentication

This paper presents a survey on the recent use of Local Binary Patterns (LBPs) for face recognition. LBP is becoming a popular technique for face representation. It is a non-parametric kernel which summarizes the local spacial structure of an image and it is invariant to monotonic gray-scale transformations. This is a very interesting property in face recognition. This probably explains the recent success of Local Binary Patterns in face recognition. In this paper, we describe the LBP technique and different approaches proposed in the literature to represent and to recognize faces. The most representatives are considered for experimental comparison on a common face authentication task. For that purpose, the XM2VTS and BANCA databases are used according to their respective experimental protocols.

/pdf/on-the-recent-use-of-local-binary-patterns-for-face-g5yuv7yi4a.pdf

On the Recent Use of Local Binary Patterns for Face Authentication

This paper studies the problem of reproducible research in remote photoplethysmography (rPPG). Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner. As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions. Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software. After a thorough, unbiased experimental evaluation in various settings, it is shown that none of the selected algorithms is precise enough to be used in a real-world scenario.

A reproducible study on remote heart rate measurement

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community.

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

In this report, we address the problem of face verification across illumination, since it has been identified as one of the major factor degrading the performance of face recognition systems. First, a brief overview of face recognition together with its main challenges is made, before reviewing state-of-the-art approaches to cope with illumination variations. We then present investigated approaches, which consists in applying a pre-processing step to the face images, and we also present the underlying theory. Namely, we will study the effect of various photometric normalization algorithms on the performance of a system based on local feature extraction and generative models (Gaussian Mixture Models). Studied algorithms include the Multiscale Retinex, as well as two state-of-the-art approaches: the Self Quotient Image and an anisotropic diffusion based normalization. This last involves the resolution of large sparse system of equations, and hence different approaches to solve such problems are described, including the efficient multigrid framework. Performances of the normalization algorithms are assessed with the challenging BANCA database and its realistic protocols. Conducted experiments showed significant improvements in terms of verification error rates and are comparable to other state-of-the-art face verification systems on the same database.

http://publications.idiap.ch/downloads/reports/2005/heusch-2005.pdf

Guillaume Heusch

Papers

Local binary patterns as an image preprocessing for face authentication

On the Recent Use of Local Binary Patterns for Face Authentication

A reproducible study on remote heart rate measurement

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

Lighting Normalization Algorithms for Face Verification