Showing papers on "Three-dimensional face recognition published in 2006"

Face Description with Local Binary Patterns: Application to Face Recognition

Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

Abstract: Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypic emotional expressions, such as anger and happiness. Instead of representing another approach to machine analysis of prototypic facial expressions of emotion, the method presented in this paper attempts to handle a large range of human facial behavior by recognizing facial muscle actions that produce expressions. Virtually all of the existing vision systems for facial muscle action detection deal only with frontal-view face images and cannot handle temporal dynamics of facial actions. In this paper, we present a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences. We exploit particle filtering to track 15 facial points in an input face-profile sequence, and we introduce facial-action-dynamics recognition from continuous video input using temporal rules. The algorithm performs both automatic segmentation of an input video into facial expressions pictured and recognition of temporal segments (i.e., onset, apex, offset) of 27 AUs occurring alone or in a combination in the input face-profile video. A recognition rate of 87% is achieved.

A review on Gabor wavelets for face recognition

Abstract: Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.

On the Use of SIFT Features for Face Authentication

Abstract: Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results.

3D Facial Expression Recognition Based on Primitive Surface Feature Distribution

Abstract: The creation of facial range models by 3D imaging systems has led to extensive work on 3D face recognition [19] However, little work has been done to study the usefulness of such data for recognizing and understanding facial expressions Psychological research shows that the shape of a human face, a highly mobile facial surface, is critical to facial expression perception In this paper, we investigate the importance and usefulness of 3D facial geometric shapes to represent and recognize facial expressions using 3D facial expression range data We propose a novel approach to extract primitive 3D facial expression features, and then apply the feature distribution to classify the prototypic facial expressions In order to validate our proposed approach, we have conducted experiments for person-independent facial expression recognition using our newly created 3D facial expression database We also demonstrate the advantages of our 3D geometric based approach over 2D texture based approaches in terms of various head poses

Three-Dimensional Face Recognition Using Shapes of Facial Curves

Abstract: We study shapes of facial surfaces for the purpose of face recognition. The main idea is to 1) represent surfaces by unions of level curves, called facial curves, of the depth function and 2) compare shapes of surfaces implicitly using shapes of facial curves. The latter is performed using a differential geometric approach that computes geodesic lengths between closed curves on a shape manifold. These ideas are demonstrated using a nearest-neighbor classifier on two 3D face databases: Florida State University and Notre Dame, highlighting a good recognition performance

Preliminary Face Recognition Grand Challenge Results

Abstract: The goal of the Face Recognition Grand Challenge (FRGC) is to improve the performance of face recognition algorithms by an order of magnitude over the best results in Face Recognition Vendor Test (FRVT) 2002. The FRGC is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with a 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 presents preliminary results of the FRGC for all six experiments. The preliminary results indicate that significant progress has been made towards achieving the stated goals.

Correlation Pattern Recognition for Face Recognition

Abstract: Two-dimensional (2-D) face recognition (FR) is of interest in many verification (1:1 matching) and identification (1:N matching) applications because of its nonintrusive nature and because digital cameras are becoming ubiquitous. However, the performance of 2-D FR systems can be degraded by natural factors such as expressions, illuminations, pose, and aging. Several FR algorithms have been proposed to deal with the resulting appearance variability. However, most of these methods employ features derived in the image or the space domain whereas there are benefits to working in the spatial frequency domain (i.e., the 2-D Fourier transforms of the images). These benefits include shift-invariance, graceful degradation, and closed-form solutions. We discuss the use of spatial frequency domain methods (also known as correlation filters or correlation pattern recognition) for FR and illustrate the advantages. However, correlation filters can be computationally demanding due to the need for computing 2-D Fourier transforms and may not match well for large-scale FR problems such as in the Face Recognition Grand Challenge (FRGC) phase-II experiments that require the computation of millions of similarity metrics. We will discuss a new method [called the class-dependence feature analysis (CFA)] that reduces the computational complexity of correlation pattern recognition and show the results of applying CFA to the FRGC phase-II data

Cascaded classification of gender and facial expression using active appearance models

Abstract: This paper presents an approach to recognising the gender and expression of face images by means of active appearance models (AAM). Features extracted by a trained AAM are used to construct support vector machine (SVM) classifiers for 4 elementary emotional states (happy, angry, sad, neutral). These classifiers are arranged into a cascade structure in order to optimise overall recognition performance. Furthermore, it is shown how performance can be further improved by first classifying the gender of the face images using an SVM trained in a similar manner. Both gender-specific expression classification and expression-specific gender classification cascades are considered, with the former yielding better recognition performance. We conclude that there are gender-specific differences in the appearance of facial expressions that can be exploited for automated recognition, and that cascades are an efficient and effective way of performing multi-class recognition of facial expressions.

Automatic feature extraction for multiview 3D face recognition

Abstract: Current 2D face recognition systems encounter difficulties in recognizing faces with large pose variations. Utilizing the pose-invariant features of 3D face data has the potential to handle multiview face matching. A feature extractor based on the directional maximum is proposed to estimate the nose tip location and the pose angle simultaneously. A nose profile model represented by subspaces is used to select the best candidates for the nose tip. Assisted by a statistical feature location model, a multimodal scheme is presented to extract eye and mouth corners. Using the automatic feature extractor, a fully automatic 3D face recognition system is developed. The system is evaluated on two databases, the MSU database (300 multiview test scans from 100 subjects) and the UND database (953 near frontal scans from 277 subjects). The automatic system provides recognition accuracy that is comparable to the accuracy of a system with manually labeled feature points.

Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition

Abstract: In this paper, we present a new method for face recognition using range data. The proposed method is based on both global statistics of geometrical features and local statistics of correlative features of facial surfaces. Firstly, we analyze the performances of common geometrical representations by using global histograms for matching. Secondly, we propose a new method to encode the relationships between points and their neighbors, which are demonstrated to own great power to represent the intrinsic structure of facial surfaces. Finally, the two kinds of features are supposed to be complementary to some extent, and the combination of them is proven to be able to improve the recognition performance. All the experiments are performed on the full 3D face dataset of FRGC 2.0 which is the largest 3D face database so far. Promising results have demonstrated the effectiveness of our proposed method.

Semi-supervised PCA-Based face recognition using self-training

Abstract: Performances of face recognition systems based on principal component analysis can degrade quickly when input images exhibit substantial variations, due for example to changes in illumination or pose, compared to the templates collected during the enrolment stage On the other hand, a lot of new unlabelled face images, which could be potentially used to update the templates and re-train the system, are made available during the system operation In this paper a semi-supervised version, based on the self-training method, of the classical PCA-based face recognition algorithm is proposed to exploit unlabelled data for off-line updating of the eigenspace and the templates Reported results show that the exploitation of unlabelled data by self-training can substantially improve the performances achieved with a small set of labelled training examples

AAM derived face representations for robust facial action recognition

Abstract: In this paper, we present results on experiments employing active appearance model (AAM) derived facial representations, for the task of facial action recognition Experimental results demonstrate the benefit of AAM-derived representations on a spontaneous AU database containing "real-world" variation Additionally, we explore a number of normalization methods for these representations which increase facial action recognition performance

Hand Posture Classification and Recognition using the Modified Census Transform

Abstract: Developing new techniques for human-computer interaction is very challenging. Vision-based techniques have the advantage of being unobtrusive and hands are a natural device that can be used for more intuitive interfaces. But in order to use hands for interaction, it is necessary to be able to recognize them in images. In this paper, we propose to apply to the hand posture classification and recognition tasks an approach that has been successfully used for face detection (B. Froba and A. Ernst, 2004). The features are based on the modified census transform and are illumination invariant. For the classification and recognition processes, a simple linear classifier is trained, using a set of feature lookup-tables. The database used for the experiments is a benchmark database in the field of posture recognition. Two protocols have been defined. We provide results following these two protocols for both the classification and recognition tasks. Results are very encouraging

Hand Vein Recognition Based on Multi Supplemental Features of Multi-Classifier Fusion Decision

Abstract: As a kind of biometric feature authentication system, hand vein recognition has more merits than others. So it has a vast foreground. In this paper, a new algorithm based on multi supplemental features of multi-classifier fusion decision is proposed. It overcomes the disadvantages of the single feature recognition. Experimental results indicate that the proposed methods can significantly improve the recognition accuracy and reliability compared to the previous hand vein recognition.

3D Face Recognition Using 3D Alignment for PCA

Abstract: This paper presents a 3D approach for recognizing faces based on Principal Component Analysis (PCA). The approach addresses the issue of proper 3D face alignment required by PCA for maximum data compression and good generalization performance for new untrained faces. This issue has traditionally been addressed by 2D data normalization, a step that eliminates 3D object size information important for the recognition process. We achieve correspondence of facial points by registering a 3D face to a scaled generic 3D reference face and subsequently perform a surface normal search algorithm. 3D scaling of the generic reference face is performed to enable better alignment of facial points while preserving important 3D size information in the input face. The benefits of this approach for 3D face recognition and dimensionality reduction have been demonstrated on components of the Face Recognition Grand Challenge (FRGC) database versions 1 and 2.

Face recognition based on a 3D morphable model

Abstract: This paper summarizes the main concepts of morphable models of 3D faces, and describes two algorithms for 3D surface reconstruction and face recognition. The first algorithm is based on an analysis-by-synthesis technique that estimates shape and pose by fully reproducing the appearance of the face in the image. The second algorithm is based on a set of feature point locations, producing high-resolution shape estimates in computation times of 0.25 seconds. A variety of different application paradigms for model-based face recognition are discussed.

Automatic 3D Face Detection, Normalization and Recognition

Abstract: A fully automatic 3D face recognition algorithm is presented. Several novelties are introduced to make the recognition robust to facial expressions and efficient. These novelties include: (1) Automatic 3D face detection by detecting the nose; (2) Automatic pose correction and normalization of the 3D face as well as its corresponding 2D face using the Hotelling transform; (3) A spherical face representation and its use as a rejection classifier to quickly reject a large number of candidate faces for efficient recognition; and (4) Robustness to facial expressions by automatically segmenting the face into expression sensitive and insensitive regions. Experiments performed on the FRGC Ver 2.0 dataset (9,500 2D/3D faces) show that our algorithm outperforms existing 3D recognition algorithms. We achieved verification rates of' 99.47% and 94.09% at 0.001 FAR and identification rates of 98.03% and 89.25% for probes with neutral and non-neutral expression respectively.

Expression-Invariant Face Recognition with Expression Classification

Abstract: Face recognition is one of the most intensively studied topics in computer vision and pattern recognition. Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. On the other hand, face geometry is a useful cue for recognition. Taking these into account, we utilize the idea of separating geometry and texture information in a face image and model the two types of information by projecting them into separate PCA spaces which are specially designed to capture the distinctive features among different individuals. Subsequently, the texture and geometry attributes are re-combined to form a classifier which is capable of recognizing faces with different expressions. Finally, by studying face geometry, we are able to determine which type of facial expression has been carried out, thus build an expression classifier. Numerical validations of the proposed method are given.

Facial Expression Recognition Based on Fusion of Multiple Gabor Features

Abstract: In order to accomplish subject-independent facial expression recognition task, a multiple Gabor features based facial expression recognition method is presented in this paper. Different channels of Gabor filters have different contributions on the facial expression recognition and reasonable combination of these features can improve the performance of a facial expression recognition system. NN based data fusion method is designed for facial expression recognition in this paper. Experimental results show that the facial expression recognition rate can be improved by using multiple channel features and neural network fusion.

Improving the Face Recognition Grand Challenge Baseline Performance using Color Configurations Across Color Spaces

Abstract: This paper presents a method that applies color information to improve face recognition performance of the face recognition grand challenge (FRGC) baseline algorithm, also known as the biometric experimentation environment (BEE) baseline algorithm. In particular, we empirically assess the face recognition performance of the BEE baseline algorithm by applying color configurations in the YIQ and the YCbCr color spaces. The color configuration is defined as an individual or a combination of color component images. Experimental results using an FRGC ver1.0 dateset containing 1,126 images demonstrate that the YQCr color configuration improves the rank-one face recognition rate of the BEE baseline algorithm from 37% to 70%; when experimenting with an FRGC ver2.0 dataset consisting of 30,702 images, the YQCr color configuration achieves 65% verification rate comparing to the FRGC baseline performance of 12%.

Highly accurate and fast face recognition using near infrared images

Abstract: In this paper, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The hardware minimizes environmental lighting and delivers face images with frontal lighting. This avoids many problems in subsequent face processing to a great extent. The face detection and recognition algorithms are based on a local feature representation. Statistical learning is applied to learn most effective features and classifiers for building face detection and recognition engines. The novel imaging system and the detection and recognition engines are integrated into a powerful face recognition system. Evaluated in real-world user scenario, a condition that is harder than a technology evaluation such as Face Recognition Vendor Tests (FRVT), the system has demonstrated excellent accuracy, speed and usability.

Highly accurate and fast face recognition using near infrared images

Abstract: In this paper, we present a highly accurate, realtime face recognition system for cooperative user applications. The novelties are: (1) a novel design of camera hardware, and (2) a learning based procedure for effective face and eye detection and recognition with the resulting imagery. The hardware minimizes environmental lighting and delivers face images with frontal lighting. This avoids many problems in subsequent face processing to a great extent. The face detection and recognition algorithms are based on a local feature representation. Statistical learning is applied to learn most effective features and classifiers for building face detection and recognition engines. The novel imaging system and the detection and recognition engines are integrated into a powerful face recognition system. Evaluated in real-world user scenario, a condition that is harder than a technology evaluation such as Face Recognition Vendor Tests (FRVT), the system has demonstrated excellent accuracy, speed and usability.