Auflistung nach Schlagwort "Face Recognition"
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- KonferenzbeitragAction-Independent Generalized Behavioral Identity Descriptors for Look-alike Recognition in Videos(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Khodabakhsh, Ali; Loiselle, HugoThere is a long history of exploitation of the visual similarity of look-alikes for fraud and deception. The visual similarity along with the application of physical and digital cosmetics greatly challenges the recognition ability of average humans. Face recognition systems are not an exception in this regard and are vulnerable to such similarities. In contrast to physiological face recognition, behavioral face recognition is often overlooked due to the outstanding success of the former. However, the behavior of a person can provide an additional source of discriminative information with regards to the identity of individuals when physiological attributes are not reliable. In this study, we propose a novel biometric recognition system based only on facial behavior for the differentiation of look-alikes in unconstrained recording conditions. To this end, we organized a dataset of 85;656 utterances from 1000 look-alike pairs based on videos collected from the wild, large enough for the development of deep learning solutions. Our selection criteria assert that for these collected videos, both state-of-the-art biometric systems and human judgment fail in recognition. Furthermore, to utilize the advantage of large-scale data, we introduce a novel action-independent biometric recognition system that was trained using triplet-loss to create generalized behavioral identity embeddings. We achieve look-alike recognition equal-error-rate of 7:93% with sole reliance on the behavior descriptors extracted from facial landmark movements. The proposed method can have applications in face recognition as well as presentation attack detection and Deepfake detection.
- KonferenzbeitragChildFace: Gender Aware Child Face Aging(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Chandaliya, Praveen Kumar; Sinha, Aditya; Nain, NeetaChild face aging and rejuvenation has amassed considerable active research interest due to its immense impact on monitoring applications especially for finding lost/abducted children with childhood photos and hence protect children. Prior studies are primarily motivated to enhance the generation quality and aging of face images, rather than quantifying face recognition performance. To address this challenge we propose ChildFace model. Our model does child face aging and rejuvenation while using gender as condition. Our model uses Conditional Generative Adversarial Nets (cGANs), VGG19 based perceptual loss and LightCNN29 age classifier and produces impressive results. Intense quantitative study based on verification, identification and age estimation proves that our model is competent to existing state-of-art models and can make a significant contribution in identifying missing children.
- KonferenzbeitragDeep Domain Adaptation for Face Recognition using images captured from surveillance cameras(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Banerjee, Samik; Bhattacharjee, Avishek; Das, SukhenduLearning based on convolutional neural networks (CNNs) or deep learning has been a major research area with applications in face recognition (FR). However, performances of algorithms designed for FR are unsatisfactory when surveillance conditions severely degrade the test probes. The work presented in this paper has three contributions. First, it proposes a novel adaptive-CNN architecture of deep learning refurbished for domain adaptation (DA), to overcome the difference in feature distributions between the gallery and probe samples. The proposed architecture consists of three components: feature (FM), adaptive (AM) and classification (CM) modules. Secondly, a novel 2-stage algorithm for Mutually Exclusive Training (2-MET) based on stochastic gradient descent, has been proposed. The final stage of training in 2-MET freezes the layers of the FM and CM, while updating (tuning) only the parameters of the AM using a few probe (as target) samples. This helps the proposed deep-DA CNN to bridge the disparities in the distributions of the gallery and probe samples, resulting in enhanced domain-invariant representation for efficient deep-DA learning and classification. The third contribution comes from rigorous experimentations performed on three benchmark real-world surveillance face datasets with various kinds of degradations. This reveals the superior performance of the proposed adaptive-CNN architecture with 2-MET training, using Rank-1 recognition rates and ROC and CMC metrics, over many recent state-of-the-art techniques of CNN and DA.
- KonferenzbeitragEfficiency Analysis of Post-quantum-secure Face Template Protection Schemes based on Homomorphic Encryption(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Kolberg, Jascha; Drozdowski, Pawel; Gomez-Barrero, Marta; Rathgeb, Christian; Busch, ChristophSince biometric characteristics are not revocable and biometric data is sensitive, privacypreserving methods are essential to operate a biometric recognition system. More precisely, the biometric information protection standard ISO/IEC IS 24745 requires that biometric templates are stored and compared in a secure domain. Using homomorphic encryption (HE), we can ensure permanent protection since mathematical operations on the ciphertexts directly correspond to those on the plaintexts. Thus, HE allows to compute the distance between two protected templates in the encrypted domain without a degradation of biometric performance with respect to the corresponding system. In this paper, we benchmark three post-quantum-secure HE schemes, and thereby show that a face verification in the encrypted domain requires only 50 ms transaction time and a template size of 5.5 KB.
- KonferenzbeitragInvestigating the Impact of Control in AI-Assisted Decision-Making - An Experimental Study(Proceedings of Mensch und Computer 2024, 2024) Meske, Christian; Ünal, ErdiWe ask whether users should adjust to AI systems or vice versa. Levels of automation (LOAs) are task dependent, may vary within one task, and also may change over time. People’s diverse abilities and preferences make the usage of AI systems possibly personal. Automation design is a complicated task. We investigate varying levels of LOAs in one specific decision-making process. For this, we conduct an experiment, where n=24 volunteers participate in a within-subject face-recognition experiment. Face-recognition is an innate ability mastered by humans. Reason are specialized neurological systems. This also makes it an intuitive task. The results show that of the five tested LOAs, each one leads to personal best and personal worst decisions regarding accuracy and time. Similarly, each LOA is preferred or opposed by participants. This shows, that there is no “one-size-fits-all” LOA, suggesting that careful design is required and multiple LOAs should be offered for a task.
- KonferenzbeitragMan vs. machine: A study comparing super-recognizers and artificial intelligence(INFORMATIK 2024, 2024) Lietsch, Maria; Preuß, Svenja; Becker, Sven; Labudde, DirkThis study addresses the limits of human and artificial intelligence (AI) in face recognition using a specially designed test, which consists of tasks regarding person identification and lookalike discrimination. It was divided into nine sets of four or five queries each. The assignments, presented in the study, were performed by ten super-recognizers from the Chemnitz police department (Saxony) as well as the AI systems “Face Recognition” and “GhostFaceNet”. The evaluation revealed considerable differences in the results of the individual super-recognizers (SR). Additionally, the comparison between human and artificial intelligence in particular revealed clear limitations of the AI in relation to the tasks set. To further evaluate the super-recognizers and AI systems, additional tests are planned, covering various topics such as the identification of siblings or the recognition of faces aged by AI.
- KonferenzbeitragA Quantum-Resistant Face Template Protection Scheme using Kyber and Saber Public Key Encryption Algorithms(BIOSIG 2022, 2022) Roberto Román, Rosario ArjonaConsidered sensitive information by the ISO/IEC 24745, biometric data should be stored and used in a protected way. If not, privacy and security of end-users can be compromised. Also, the advent of quantum computers demands quantum-resistant solutions. This work proposes the use of Kyber and Saber public key encryption (PKE) algorithms together with homomorphic encryption (HE) in a face recognition system. Kyber and Saber, both based on lattice cryptography, were two finalists of the third round of NIST post-quantum cryptography standardization process. After the third round was completed, Kyber was selected as the PKE algorithm to be standardized. Experimental results show that recognition performance of the non-protected face recognition system is preserved with the protection, achieving smaller sizes of protected templates and keys, and shorter execution times than other HE schemes reported in literature that employ lattices. The parameter sets considered achieve security levels of 128, 192 and 256 bits.
- KonferenzbeitragTime flies by: Analyzing the Performance Impact of Ageing in Face Recognition with Synthetic Data(BIOSIG 2022, 2022) Marcel Grimmer, Haoyu ZhangThe vast progress in synthetic image synthesis enables the generation of facial images in high resolution and photorealism. In biometric applications, the main motivation for using synthetic data is to solve the shortage of publicly-available biometric data while reducing privacy risks when processing such sensitive information. These advantages are exploited in this work by simulating human face ageing with recent face age modification algorithms to generate mated samples, thereby studying the impact of ageing on the performance of an open-source biometric recognition system. Further, a real dataset is used to evaluate the effects of short-term ageing, comparing the biometric performance to the synthetic domain. The main findings indicate that short-term ageing in the range of 1-5 years has only minor effects on the general recognition performance. However, the correct verification of mated faces with long-term age differences beyond 20 years poses still a significant challenge and requires further investigation.
- KonferenzbeitragWorst-Case Morphs: a Theoretical and a Practical Approach(BIOSIG 2022, 2022) Una Kelly, Luuk Spreeuwers and Raymond VeldhuisFace Recognition (FR) systems have been shown to be vulnerable to morphing attacks. We examine exactly how challenging morphs can become. By showing a worst-case construction in the embedding space of an FR system and using a mapping from embedding space back to image space we generate images that show that this theoretical upper bound can be approximated if the FR system is known. The resulting morphs can also succesfully fool unseen FR systems and are useful for exploring and understanding the weaknesses of FR systems. Our method contributes to gaining more insight into the vulnerability of FR systems.