In this paper, we propose a precise and fast eye center localization method. The method is based on SDM [1] algorithm, which has achieved impressive performance in face alignment area. In this work, we further improve the basic SDM algorithm in two aspects: 1) employ the coarse to fine strategy to enhance the robustness to initialization; 2) utilize multiple nonlinear features to ensure the robustness while improve the accuracy. Experimental results on BioID database shows that our improved SDM achieves preferable localization accuracy against the state-of-the-art methods at a speed as fast as 2ms to locate two eyes.

In this paper, we propose a novel method for generating a realistic 3D human face from a single 2D face image for the purpose of synthesizing new 2D face images at arbitrary poses using gender and ethnicity specific models. We employ the Generic Elastic Model (GEM) approach, which elastically deforms a generic 3D depth-map based on the sparse observations of an input face image in order to estimate the depth of the face image. Particularly, we show that Gender and Ethnicity specific GEMs (GE-GEMs) can approximate the 3D shape of the input face image more accurately, achieving a better generalization of 3D face modeling and reconstruction compared to the original GEM approach. We qualitatively validate our method using publicly available databases by showing each reconstructed 3D shape generated from a single image and new synthesized poses of the same person at arbitrary angles. For quantitative comparisons, we compare our synthesized results against 3D scanned data and also perform face recognition using synthesized images generated from a single enrollment frontal image. We obtain promising results for handling pose and expression changes based on the proposed method.

This paper provides an in-depth analysis on face shape alignment for pose insensitive face recognition. The dissimilarity between two face images can be modeled as the difference in intensity between these two images, obtained by warping these faces onto the same shape. In order to achieve this, we must first align both face images independently to obtain a sparse 2-D shape representation. We achieve this by using a Combination of ASMs and AAMs (CASAAMs). We then exchange these two shapes and obtain new intensity (texture) faces based on these exchanged shapes. This allows us to align the two faces with increased pixel-level correspondence while simultaneously achieving a certain degree of pose correction. In order to account for large pose variation, it becomes necessary to model the underlying 3-D face structure for the synthesis of novel 2-D poses. However, in many real-world scenarios, only a single image of the subject is provided and acquisition of the 3-D model is not always feasible. To tackle this common real-world scenario, we propose a novel approach for modeling faces, called 3D Generic Elastic Model (3D-GEM), which can be deformed from a single 2-D image. Our analysis shows that 3-D depth information of human faces does not dramatically change across people, indicating that precise depth information of a person is not needed to generate useful novel 2-D poses. This is a significant discovery that makes our method feasible. We thus demonstrate that our 3-D face model can be efficiently produced by using a generic depth model which can be elastically deformed based on input facial features. This face model can then be rotated in 3-D in order to synthesize any arbitrary 2-D facial pose. Experimental results show that 3-D faces modeled by our proposed work effectively handle large 3-D pose changes in face alignment and they can be used for achieving pose tolerant face recognition. We also provide comparative results of face synthesis obtained by an actual 3-D face scanner and our approach, showing our proposed modeling approach is both effective and efficient.

This paper proposes an efficient way of modeling 3D faces by using only two - a frontal and a profile - images. Although it is desirable to utilize only one single image for 3D face modeling, more accurate depth information can be obtained if we use a profile face image additionally. Despite this seemly straightforward task, however, no standard solutions for 3D face modeling with two images have yet been reported. To tackle this problem, in our work, we first extract facial shape information from each image and then align these two shapes in order to obtain a sparse 3D face. Then, the observed sparse 3D face is combined into generic dense depth information. By doing so, we reflect both the observed 3D sparse depth information and smooth depth changes around facial areas in our reconstructed 3D shape. Finally, the intensity of the frontal image is texture-mapped onto the reconstructed 3D shape for realistic 3D modeling. Unlike other 3D modeling methods, our proposed work is extremely fast (within a few seconds) and does not require any complex hardware settings or calibration. We illustrate our 3D modeling results by using the MPIE-database and demonstrate the effectiveness of the proposed approach.

Generic 3D face pose estimation from a single 2D facial image is an extremely crucial requirement for face-related research areas. To meet with the remaining challenges for face pose estimation, suggested Murphy-Chutorian et al. [13], we believe that the first step is to create a large corpus of a 3D facial shape database in which the statistical relationship between projected 2D shapes and corresponding pose parameters can be easily observed. Because fa- cial geometry provides the most essential information for facial pose, understanding the effect of pose parameters in 2D facial shapes is a key step toward solving the remaining challenges. In this paper, we present necessary tasks to reconstruct 3D facial shapes from multiple 2D images and then explain how to generate 2D projected shapes at any rotation interval. To deal with self occlusions, a novel hidden points removal (HPR) algorithm is also proposed. By flexibly changing the number of points in 2D shapes, we evaluate the performance of two different approaches for achieving generic 3D pose estimation in both coarse and fine levels and analyze the importance of facial shapes toward generic 3D pose estimation.

Traditional approaches to face recognition have utilized aligned facial images containing both shape and texture information. This paper analyzes the contributions of the individual facial shape and texture components to face recognition. These two components are evaluated independently and we investigate methods to combine the information gained from each of them to enhance face recognition performance. The contributions of this paper are the following: (1) to the best of our knowledge, it is the first large-scale study of how face recognition is influenced by shape and texture as all of our results are benchmarked against traditional approaches on the challenging NIST FRGC ver2.0 experiment 4 dataset, (2) we empirically show that shape information is reasonably discriminative, (3) we demonstrate significant improvement in performance by registering texture with dense shape information, and finally (4) show that fusing shape and texture information consistently boosts recognition results across different subspace-based algorithms.

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.

Recently, Direct Linear Discriminant Analysis (D-LDA) and Gram-Schmidt LDA methods have been proposed for face recognition. By also utilizing some of the null-space of the within-class scatter matrix, they exhibit better performance compared to Fisherfaces and Eigenfaces. However, these linear subspace methods may not discriminate faces well due to large nonlinear distortions in the face images. Redundant class dependence feature analysis (CFA) method exhibits superior performance compared to other methods by representing nonlinear features well. We show that with a proper choice of kernel parameters used with the proposed Kernel Correlation Filters within the CFA framework, the overall face recognition performance is significantly improved. We present results of this proposed approach on a large scale database from the Face Recognition Grand Challenge (FRGC) which contains over 36,000 images.

Face recognition is one of the least intrusive biometric modalities that can be used to identify individuals from surveillance video. In such scenarios the users are under the least co-operative conditions and thus the ability to perform robust face recognition in such scenarios is very challenging. In this paper we focus on improving the face recognition performance on a large database with over 36,000 facial images from the Face Recognition Grand Challenge Phase-II data collected by University of Notre Dame. We particularly focus on Experiment 4 which is the most challenging and captured in uncontrolled conditions where the baseline PCA algorithm yields 12% verification rate at 0.1% FAR. We propose a novel approach using class-dependent kernel discrete cosine transform features which improves the performance significantly yielding a 91.33% verification rate at 0.1% FAR, and we also show that by working in the DCT transform domain for obtaining non-linear features is more optimal than working in the original spatial-pixel domain which only yields a verification rate of 85% at 0.1% FAR. Thus our proposed method outperforms the baseline by 79.33% in verification rate @0.1% False Acceptance Rate.

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.