Twins 3D face recognition challenge (original) (raw)
Related papers
A Survey on Face Recognition of Identical Twins
2015
Recent studies have shown that face recognition performance degrades considerably for images of identical twins. Human face matching capability is often taken into consideration as a bench-mark for assessing and improving automatic face recognition algorithms. Here, this paper will show human capability to distinguish between identical twins. If humans are able to distinguish between facial images of identical twins, it would suggest that humans are capable of identifying discriminating facial traits that can potentially be useful to develop algorithms for this very challenging problem. If humans viewing a pair of facial images can perceive if the image pairs belong to the same person or to a pair of identical twins. The paper consists of experiments results, which are conducted on 186 twin subjects, making it the largest such study in the literature to date. And observation will show that humans can perform the task significantly better if they are given enough time and tend to mak...
A group of facial normal descriptors for recognizing 3D identical twins
2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2012
In this paper, to characterize and distinguish identical twins, three popular texture descriptors: i.e. local binary patterns (LBPs), gabor filters (GFs) and local gabor binary patterns (LGBPs) are employed to encode the normal components (x, y and z) of the 3D facial surfaces of identical twins respectively. A group of facial normal descriptors are thus achieved, including Normal Local Binary Patterns descriptor (N-LBPs), Normal Gabor Filters descriptor (N-GFs) and Normal Local Gabor Binary Patterns descriptor (N-LGBPs). All these normal encoding based descriptors are further fed into sparse representation classifier (SR-C) for identification. Experimental results on the 3D TEC database demonstrate that these proposed normal encoding based descriptors are very discriminative and efficient, achieving comparable performance to the best of state-ofthe-art algorithms.
Identical Twins as a Facial Similarity Benchmark for Human Facial Recognition
2021 International Conference of the Biometrics Special Interest Group (BIOSIG), 2021
The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. Due to the high facial similarity of both identical twins and look-alikes, these face pairs represent the hardest cases presented to facial recognition tools. The effect of these highly similar face pairs on these tools is important to investigate to ensure that facial recognition tools can adequately address these problems. Additionally, analyzing the facial similarity of these face pairs allows for a better understanding of the differences between the comparison score returned by a facial recognition tool, and a similarity score based on the perceived facial similarity of the faces in question. This analysis allows for an investigation into the role that facial similarity plays in the determination of face comparison scores in any face recognition approach. This work presents an application of one of the largest twin datasets compiled to date to address two FR challenges: 1) determining a baseline measure of facial similarity between identical twins and 2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face datasets. The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs. An additional analysis which correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed.
Facial recognition of identical twins
2011 International Joint Conference on Biometrics (IJCB), 2011
Biometric identification systems must be able to distinguish between individuals even in situations where the biometric signature may be similar, such as in the case of identical twins. This paper presents experiments done in facial recognition using data from a set of images of twins. This work establishes the current state of facial recognition in regards to twins and the accuracy of current state-of-theart programs in distinguishing between identical twins using three commercial face matchers, Cognitec 8.3.2.0, Ver-iLook 4.0, and PittPatt 4.2.1 and a baseline matcher employing Local Region PCA. Overall, it was observed that Cognitec had the best performance. All matchers, however, saw degradation in performance compared to an experiment where the ability to distinguish unrelated persons was assessed. In particular, lighting and expression seemed to have affected performance the most.
Differentiating Identical Twins by Using Conditional Face Recognition Algorithms
2015
Reliable and accurate verification of people is extremely important in a number of business transaction as well as access to privileged information. Identical twins have the closest genetics-based relationship and therefore, the maximum similarity between face is expected to be found among identical twins. This paper presents facial features, which has important for the acceptance of expert proof in legal proceedings for determining the identity of an individual from facial images. Our experiments show that modal of face recognition systems can distinguish two different person who are identical twins. We show the effect of using a variety of facial surface representation and suggest a method of identifying identical twins. Performance results are broken out by lighting, expression, gender
Procedings of the British Machine Vision Conference 2006, 2006
In this paper, we present a new 3D face recognition approach. Full automation is provided through the use of advanced multi-stage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact wavelet metadata. We present results on the largest known, and now publicly-available, Face Recognition Grand Challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, our approach has achieved the highest accuracy on this dataset.
An Open Platform for 3D Face Recognition Algorithms
Proceedings of the 1st International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 19-20 October 2010, 2010
In this paper we describe a new open platform designed to integrate 3D face matching algorithms for recognition. Its main purpose is to provide experimental environment to online operational testing of 3D face recognition approaches, in laboratory conditions. The proposed platform consists of: (i) An acquisition module that interfaces with Minolta 3D laser-based scanner, (ii) A preprocessing subsystem allowing detection and segmentation of the useful part of the face from the depth image (scanner's output) and its processing (iii) A face matching module that incorporates matching algorithms, and (iv) A decision component that provides the final matching result. Moreover, we show an integration example of our algorithm [1] and discuss experimental results. Our 3D facial matching algorithm currently integrated to the proposed platform represents facial surfaces by indexed collections of radial curves on them, emanating from the nose tips, and compares the facial shapes by comparing the shapes of their corresponding curves. Using a framework on elastic shape analysis of curves, we obtain an algorithm for comparing facial surfaces. We also introduce a quality control module which allows our approach to be robust to pose variations and missing data. Comparative evaluation using a common experimental setup on GavabDB 1 dataset, considered as the most expression-rich and noise-prone 3D face dataset, shows that our approach outperforms other state-of-the-art approaches.
Automatic Asymmetric 3D-2D Face Recognition
2010
3D Face recognition has been considered as a major solution to deal with unsolved issues of reliable 2D face recognition in recent years, i.e. lighting and pose variations. However, 3D techniques are currently limited by their high registration and computation cost. In this paper, an asymmetric 3D-2D face recognition method is presented, enrolling in textured 3D whilst performing automatic identification using only 2D facial images. The goal is to limit the use of 3D data to where it really helps to improve face recognition accuracy. The proposed approach contains two separate matching steps: Sparse Representation Classifier (SRC) is applied to 2D-2D matching, while Canonical Correlation Analysis (CCA) is exploited to learn the mapping between range LBP faces (3D) and texture LBP faces (2D). Both matching scores are combined for the final decision. Moreover, we propose a new preprocessing pipeline to enhance robustness to lighting and pose effects. The proposed method achieves better experimental results in the FRGC v2.0 dataset than 2D methods do, but avoiding the cost and inconvenience of data acquisition and computation of 3D approaches.
A study of face recognition of identical twins by humans
2011 IEEE International Workshop on Information Forensics and Security, 2011
Recent studies have shown that face recognition performance degrades considerably for images of identical twins. Human face matching capability is often considered as a benchmark for assessing and improving automatic face recognition algorithms. In this work, we investigate human capability to distinguish between identical twins. If humans are able to distinguish between facial images of identical twins, it would suggest that humans are capable of identifying discriminating facial traits that can potentially be useful to develop algorithms for this very challenging problem. Experiments with different viewing times and imaging conditions are conducted to determine if humans viewing a pair of facial images can perceive if the image pairs belong to the same person or to a pair of identical twins. The experiments are conducted on 186 twin subjects, making it the largest such study in the literature to date. We observe that humans can perform the task significantly better if they are given enough time and tend to make more mistakes when images differ in imaging conditions. Our analysis also suggests that humans look for facial marks like moles, scars, etc. to make their decision and do worse when presented with images lacking such marks. Experiments with automatic face recognition systems show that human observers outperform automatic matchers for this task.
A Critical Assessment of 2D and 3D Face Recognition Algorithms
2009
We present the results of a project aimed to evaluate 2D and 3D face recognition algorithms. In particular, we focused on the potentialities of 3D-based techniques to overcome typical limitations of 2D methods in non-controlled situations. According to the reference scenario of people identification at airport check points, we built a representative database on which we tested different face recognition algorithms. We implemented and tested an improved version of a well-known state-of-the-art 3D approach, and verified that on our dataset it performs better than a widely used commercial system.