An official website of the United States government
By combining two or more face images of look-alikes, morphed face images are generated to fool Facial Recognition Systems (FRS) into falsely accepting multiple people, leading to failures in security systems. Despite several attempts in the literature, finding pairs of bona fide faces to generate the morphed images is still a challenging problem. In this paper, we morph identical twin pairs to generate extremely difficult morphs for FRS. We first explore three methods of morphed face generation, GAN-based, landmark-based, and a wavelet-based morphing approach. We leverage these methods to generate morphs from the identical twin pairs that retain high similarity to both subjects while resulting in minimal artifacts in the visual domain. To further improve the difficulty of recognizing morphed face images, we perform an ablation study to apply adversarial perturbation to the morphs such that they cannot be detected by trained morph classifiers. The evaluation of the generated identical twin-morphed dataset is performed in terms of vulnerability analysis and presentation attack error rates.
more »
« less
Benchmarking human face similarity using identical twins
Abstract 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. This work presents an application of one of the largest twin data sets 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 data sets. 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 data sets to identify similar face pairs. An additional analysis that correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed.
more »
« less
- Award ID(s):
- 1650474
- PAR ID:
- 10376002
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Biometrics
- Volume:
- 11
- Issue:
- 5
- ISSN:
- 2047-4938
- Format(s):
- Medium: X Size: p. 459-484
- Size(s):
- p. 459-484
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Although face recognition (FR) has achieved great success in recent years, it is still challenging to accurately recognize faces in low-quality images due to the obscured facial details. Nevertheless, it is often feasible to make predictions about specific soft biometric (SB) attributes, such as gender, age, and baldness even in dealing with low-quality images. In this paper, we propose a novel multi-branch neural network that leverages SB attribute information to boost the performance of FR. To this ed, we propose a cross-attribute-guided transformer fusion (CATF) module that effectively captures the long-range dependencies and relationships between FR and SB feature representations. The synergy created by the reciprocal flow of information in the dual cross-attention operations of the proposed CATF module enhances the performance of FR. Furthermore, we introduce a novel self-attention distillation framework that effectively highlights crucial facial regions, such as landmarks by aligning low-quality images with those of their high-quality counterparts in the feature space. The proposed self-attention distillation regularizes our network. to learn a unified quality-invariant feature representation in unconstrained environments. We conduct extensive experiments on various real-world FR benchmarks varying in quality. Experimental results demonstrate the superiority of our FR method compared to state-of-the-art FR studies.more » « less
-
In this paper we proposed a real-time face mask detection and recognition for CCTV surveillance camera videos. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, face mask detection, and face recognition. In the first step, a set of keyframes are selected using histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (CLAHE), and poses normalization (Angular Affine Transformation). In third step, facial parts are segmented using clustering approach i.e. Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, Pixel-based Feature Extraction is performed using Yolo Nano approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. The final step is face recognition which is implemented by a Kernel-based Extreme Learning Machine with Slime Mould Optimization (SMO). Experiments conducted using Python IDLE 3.8 for the proposed Yolo Nano model and also previous works as GMM with Deep learning (GMM+DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics.more » « less
-
Facial recognition technology is becoming increasingly ubiquitous nowadays. Facial recognition systems rely upon large amounts of facial image data. This raises serious privacy concerns since storing this facial data securely is challenging given the constant risk of data breaches or hacking. This paper proposes a privacy-preserving face recognition and verification system that works without compromising the user’s privacy. It utilizes sensor measurements captured by a lensless camera - FlatCam. These sensor measurements are visually unintelligible, preserving the user’s privacy. Our solution works without the knowledge of the camera sensor’s Point Spread Function and does not require image reconstruction at any stage. In order to perform face recognition without information on face images, we propose a Discrete Cosine Transform (DCT) domain sensor measurement learning scheme that can recognize faces without revealing face images. We compute a frequency domain representation by computing the DCT of the sensor measurement at multiple resolutions and then splitting the result into multiple subbands. The network trained using this DCT representation results in huge accuracy gains compared to the accuracy obtained after directly training with sensor measurement. In addition, we further enhance the security of the system by introducing pseudo-random noise at random DCT coefficient locations as a secret key in the proposed DCT representation. It is virtually impossible to recover the face images from the DCT representation without the knowledge of the camera parameters and the noise locations. We evaluated the proposed system on a real lensless camera dataset - the FlatCam Face dataset. Experimental results demonstrate the system is highly secure and can achieve a recognition accuracy of 93.97% while maintaining strong user privacy.more » « less
-
Abstract A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks.more » « less
