Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

Filters

3
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Hand Shape Recognition Using Very Deep Convolutional Neural Networks
    ( 2018)
    Rakowski, Alexander
    ;
    Wandzik, Lukasz
    This work examines the application of modern deep convolutional neural network architectures for classification tasks in the sign language domain. Transfer learning is performed by pre-training the models on the ImageNet dataset. After fine-tuning on the ASL fingerspelling and the 1 Million Hands datasets the models outperform state-of-the-art approaches on both hand shape classification tasks. Introspection of the trained models using Saliency Maps is also performed to analyze how the networks make their decisions. Finally, their robustness is investigated by occluding selected image regions.
  • Publication
    Morphing Detection Using a General-Purpose Face Recognition System
    ( 2018)
    Wandzik, Lukasz
    ;
    Kaeding, Gerald
    ;
    Vicente-Garcia, Raul
    Image morphing has proven to be very successful at deceiving facial recognition systems. Such a vulnerability can be critical when exploited in an automatic border control scenario. Recent works on this topic rely on dedicated algorithms which require additional software modules deployed alongside an existing facial recognition system. In this work, we address the problem of morphing detection by using state-of-the-art facial recognition algorithms based on hand-crafted features and deep convolutional neural networks. We show that a general-purpose face recognition system combined with a simple linear classifier can be successfully used as a morphing detector. The proposed method reuses an existing feature extraction pipeline instead of introducing additional modules. It requires neither fine-tuning nor modifications to the existing recognition system and can be trained using only a small dataset. The proposed approach achieves state-of-the-art performance on our morphing datasets using a 5-fold cross-validation.
  • Publication
    CNNs under attack. On the vulnerability of deep neural networks based face recognition to image morphing
    ( 2017)
    Wandzik, Lukasz
    ;
    Vicente-Garcia, Raul
    ;
    Kaeding, Gerald
    ;
    Chen, Xi
    Facial recognition has become a critical constituent of common automatic border control gates. Despite many advances in recent years, face recognition systems remain susceptible to an ever evolving diversity of spoofing attacks. It has recently been shown that high-quality face morphing or splicing can be employed to deceive facial recognition systems in a border control scenario. Moreover, facial morphs can easily be produced by means of open source software and with minimal technical knowledge. The purpose of this work is to quantify the severeness of the problem using a large dataset of morphed face images. We employ a state-of-the-art face recognition algorithm based on deep convolutional neural networks and measure its performance on a dataset of 7260 high-quality facial morphs with varying blending factor. Using the Inception-ResNet-v1 architecture we train a deep neural model on 4 million images to obtain a validation rate of 99.96% at 0.04% false acceptance rate (FAR) on the original, unmodified images. The same model fails to repel 1.13% of all morphing attacks, accepting both the impostor and the document owner. Based on these results, we discuss the observed weaknesses and possible remedies.