Robust 3D Face Recognition Research Articles

Robust 3D face recognition in unconstrained environment using distance based ternary search siamese network

Robust 3D face recognition in unconstrained environment using distance based ternary search siamese network

A fast and robust 3D face recognition approach based on deeply learned face representation

With the superiority of three-dimensional (3D) scanning data, e.g., illumination invariance and pose robustness, 3D face recognition theoretically has the potential to achieve better results than two-dimensional (2D) face recognition. However, traditional 3D face recognition techniques suffer from high computational costs. This paper proposes a fast and robust 3D face recognition approach with three component technologies: a fast 3D scan preprocessing, multiple data augmentation, and a deep learning technique based on facial component patches. First, unlike the majority of the existing approaches, which require accurate facial registration, the proposed approach uses only three facial landmarks. Second, the specifical deep network with an improved supervision is designed to extract complementary features from four overlapping facial component patches. Finally, a data augmentation technique and three self-collected 3D face datasets are used to enlarge the scale of the training data. The proposed approach outperforms the state-of-the-art algorithms on four public 3D face benchmarks, i.e., 100%, 99.75%, 99.88%, and 99.07% rank-1 IRs with the standard test protocol on the FRGC v2.0, Bosphorus, BU-3DFE, and 3D-TEC datasets, respectively. Further, it requires only 0.84 seconds to identify a probe from a gallery with 466 faces.

Nasal Patches and Curves for Expression-Robust 3D Face Recognition.

The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmarking algorithm detects seven keypoints on the nasal region. In the third step, a feature extraction algorithm based on the surface normals of Gabor-wavelet filtered depth maps is utilised and, then, a set of spherical patches and curves are localised over the nasal region to provide the feature descriptors. The last step applies a genetic algorithm-based feature selector to detect the most stable patches and curves over different facial expressions. The algorithm provides the highest reported nasal region-based recognition ranks on the FRGC, Bosphorus and BU-3DFE datasets. The results are comparable with, and in many cases better than, many state-of-the-art 3D face recognition algorithms, which use the whole facial domain. The proposed method does not rely on sophisticated alignment or denoising steps, is very robust when only one sample per subject is used in the gallery, and does not require a training step for the landmarking algorithm.

Robust 3D Face Recognition in Presence of Pose and Partial Occlusions or Missing Parts

In this paper, we propose a robust 3D face recognition system which can handle pose as well as occlusions in real world. The system at first takes as input, a 3D range image, simultaneously registers it using ICP(Iterative Closest Point) algorithm. ICP used in this work, registers facial surfaces to a common model by minimizing distances between a probe model and a gallery model. However the performance of ICP relies heavily on the initial conditions. Hence, it is necessary to provide an initial registration, which will be improved iteratively and finally converge to the best alignment possible. Once the faces are registered, the occlusions are automatically extracted by thresholding the depth map values of the 3D image. After the occluded regions are detected, restoration is done by Principal Component Analysis (PCA). The restored images, after the removal of occlusions, are then fed to the recognition system for classification purpose. Features are extracted from the reconstructed non-occluded face images in the form of face normals. The experimental results which were obtained on the occluded facial images from the Bosphorus 3D face database, illustrate that our occlusion compensation scheme has attained a recognition accuracy of 91.30%.

3D face recognition using local binary patterns

It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. In this paper, we develop a novel 3D face recognition algorithm using Local Binary Pattern (LBP) under expression varieties, which is an extension of the LBP operator widely used in ordinary facial analysis. First, to depict the human face more accurately and reduce the effect of facial local distortion for face recognition, a special feature-based 3D face division scheme is proposed. Then, the LBP representation framework for 3D faces is described, and the facial depth and normal information are extracted and encoded by LBP, to reduce the expression effect. For each face region, the statistical histogram is utilized to summarize the facial details, and accordingly three matching strategies are presented to address the recognition task. Finally, the proposed 3D face recognition algorithm is tested on BJUT-3D and FRGC v2.0 databases, achieves promising results, and concludes that it is feasible and valid to apply the LBP representation framework on 3D face recognition.

Robust 3D face recognition based on resolution invariant features

A novel resolution invariant local feature based method is proposed for 3D face recognition. Scale space extrema on shape index images and texture images are detected and matched, through which resolution and noise insensitive face matching is achieved without complex preprocessing and normalization. An outlier removal strategy is designed to eliminate incorrect matching points while keeping relevant ones. Six different scale invariant similarity measures are proposed and fused at the score level, which increases the robustness against expression variations. Systematical experiments are conducted on the FRGC v2.0 database, achieving in the neutral vs. all experiment a verification rate of 90.7% with un-normalized similarity scores, and 96.3% with normalized similarity scores at False Acceptance Rate (FAR) of 0.1%, and 96.2% rank-1 identification rate, which are comparable to the state of the art, and promising considering the significantly reduced preprocessing requirement.

Robust 3D Face Recognition by Local Shape Difference Boosting

This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is self-dependent and avoids the registration between an input face against every face in the gallery. Then, a Signed Shape Difference Map (SSDM) is computed between two aligned 3D faces as a mediate representation for the shape comparison. Based on the SSDMs, three kinds of features are used to encode both the local similarity and the change characteristics between facial shapes. The most discriminative local features are selected optimally by boosting and trained as weak classifiers for assembling three collective strong classifiers, namely, CSDCs with respect to the three kinds of features. Different schemes are designed for verification and identification to pursue high performance in both recognition and computation. The experiments, carried out on FRGC v2 with the standard protocol, yield three verification rates all better than 97.9 percent with the FAR of 0.1 percent and rank-1 recognition rates above 98 percent. Each recognition against a gallery with 1,000 faces only takes about 3.6 seconds. These experimental results demonstrate that our algorithm is not only effective but also time efficient.

A Robust 3D Face Recognition Algorithm Using Passive Stereo Vision

The recognition performance of the conventional 3D face recognition algorithm using ICP (Iterative Closest Point) is degraded for the 3D face data with expression changes. Addressing this problem, we consider the use of the expression-invariant local regions of a face. We find the expression-invariant regions through the distance analysis between 3D face data with the neutral expression and smile, and propose a robust 3D face recognition algorithm using passive stereo vision. We demonstrate efficient recognition performance of the proposed algorithm compared with the conventional ICP-based algorithm through the experiment using a stereo face image database which includes the face images with expression changes.

3D face recognition by constructing deformation invariant image

Based on the observation that facial surfaces across different expressions can be modeled as similar isometric transformations, in this paper a novel deformation invariant image for robust 3D face recognition is proposed. First, we obtain the depth image and the intensity image from the original 3D facial data. Then, geodesic level curves are generated by constructing radial geodesic distance image from the depth image. Finally, deformation invariant image is constructed by evenly sampling points from the selected geodesic level curves in the intensity image. Our experiments are based on the 3D CASIA Face Database, which includes 123 individuals with complex expressions. Experimental results show that our proposed method substantially improves the recognition performance under various facial expressions.