http://dl.gi.de/handle/20.500.12116/23780 2026-07-21T13:43:46Z 2026-07-21T13:43:46Z Zeinstra, Chris Haasnoot, Erwin http://dl.gi.de/handle/20.500.12116/23812 2019-06-17T10:00:30Z 2018-01-01T00:00:00Z

Shallow CNNs for the Reliable Detection of Facial Marks Zeinstra, Chris; Haasnoot, Erwin Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas Facial marks are local irregularities of skin texture. Their type and/or spatial pattern can be used as a (soft) biometric modality in several applications. A key requirement for a biometric system that utilises facial marks is their reliable detection. Detection methods typically use a blob detector followed by heuristic post processing steps to reduce the number of false positives. In this paper, we consider shallow Convolutional Neural Networks (CNNs) for facial mark detection. The choice of this network type seems natural as it learns multiple (non) blob detectors; shallow refers to the fact that we only consider CNNs up to three layers.We show that (a) these CNNs successfully address the false positive problem, (b) remove the need for post processing steps, and (c) outperform a classic blob detector, approaches taken in previous studies and some other non CNN type classifiers in terms of EER and FMR at TMR=0.95.

2018-01-01T00:00:00Z Khodabakhsh, Ali Ramachandra, Raghavendra Raja, Kiran Wasnik, Pankaj Busch, Christoph http://dl.gi.de/handle/20.500.12116/23809 2019-06-17T10:00:29Z 2018-01-01T00:00:00Z

Fake Face Detection Methods: Can They Be Generalized? Khodabakhsh, Ali; Ramachandra, Raghavendra; Raja, Kiran; Wasnik, Pankaj; Busch, Christoph Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas With advancements in technology, it is now possible to create representations of human faces in a seamless manner for fake media, leveraging the large-scale availability of videos. These fake faces can be used to conduct personation attacks on the targeted subjects. Availability of open source software and a variety of commercial applications provides an opportunity to generate fake videos of a particular target subject in a number of ways. In this article, we evaluate the generalizability of the fake face detection methods through a series of studies to benchmark the detection accuracy. To this extent, we have collected a new database of more than 53;000 images, from 150 videos, originating from multiple sources of digitally generated fakes including Computer Graphics Image (CGI) generation and many tampering based approaches. In addition, we have also included images (with more than 3;200) from the predominantly used Swap-Face application that is commonly available on smart-phones. Extensive experiments are carried out using both texture-based handcrafted detection methods and deep learning based detection methods to find the suitability of detection methods. Through the set of evaluation, we attempt to answer if the current fake face detection methods can be generalizable.

2018-01-01T00:00:00Z Kazemi, Hadi Taherkhani, Fariborz Nasrabadi, Nasser M. http://dl.gi.de/handle/20.500.12116/23810 2019-06-17T10:00:29Z 2018-01-01T00:00:00Z

Unsupervised Facial Geometry Learning for Sketch to Photo Synthesis Kazemi, Hadi; Taherkhani, Fariborz; Nasrabadi, Nasser M. Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry where the goal is to learn the mapping between a face sketch image and its corresponding photo-realistic image. However, the limited number of paired sketch-photo training data usually prevents the current frameworks to learn a robust mapping between the geometry of sketches and their matching photo-realistic images. Consequently, in this work, we present an approach for learning to synthesize a photo-realistic image from a face sketch in an unsupervised fashion. In contrast to current unsupervised image-to-image translation techniques, our framework leverages a novel perceptual discriminator to learn the geometry of human face. Learning facial prior information empowers the network to remove the geometrical artifacts in the face sketch.We demonstrate that a simultaneous optimization of the face photo generator network, employing the proposed perceptual discriminator in combination with a texture-wise discriminator, results in a significant improvement in quality and recognition rate of the synthesized photos. We evaluate the proposed network by conducting extensive experiments on multiple baseline sketch-photo datasets.

2018-01-01T00:00:00Z Banerjee, Samik Bhattacharjee, Avishek Das, Sukhendu http://dl.gi.de/handle/20.500.12116/23811 2019-06-17T10:00:30Z 2018-01-01T00:00:00Z

Deep Domain Adaptation for Face Recognition using images captured from surveillance cameras Banerjee, Samik; Bhattacharjee, Avishek; Das, Sukhendu Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Rathgeb, Christian; Uhl, Andreas Learning 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.

2018-01-01T00:00:00Z