Manuel Günther

Manuel Günther
University of Zurich | UZH · Department of Informatics - IFI

Doctor of Engineering

About

48
Publications
33,466
Reads
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1,353
Citations
Introduction
Manuel Günther is Assistant Professor for Artificial Intelligence and Machine Learning at the Department of Informatics at the University of Zurich.
Additional affiliations
July 2020 - present
University of Zurich
Position
  • Professor (Assistant)
Description
  • Group leader of the Artificial Intelligence and Machine Learning Group
September 2015 - October 2018
University of Colorado Colorado Springs
Position
  • Research Associate
Description
  • Face recognition using facial attributes extracted with deep convolutional neural networks
January 2012 - June 2015
Idiap Research Institute
Position
  • PostDoc Position
Description
  • Implementing open source biometric (in particular face) recognition algorithms: http://pypi.python.org/pypi/bob.bio.base
Education
September 1999 - April 2005
Technische Universität Ilmenau
Field of study
  • Computer Science

Publications

Publications (48)
Article
Full-text available
Clustering is a critical part of many tasks and, in most applications, the number of clusters in the data are unknown and must be estimated. This paper presents an Extreme Value Theory-based approach to threshold selection for clustering, proving that the “correct” linkage distances must follow a Weibull distribution for smooth feature spaces. Deep...
Conference Paper
Facial recognition systems have considerably evolved in recent years and have been deployed in a number of real-world applications requiring high security: bank account access, smartphone unlock, and border control, among others. In spite of their advantages, those biometric systems are still vulnerable to attack presentations which can be easily l...
Conference Paper
In real-world deployments, machine learning applications find challenges when accessing ever-increasing volumes of data – the real world is open and often presents data from classes not seen in training. Open-set recognition is a growing area of machine learning addressing such problems. This research work advances the state-of-the-art in open-set...
Conference Paper
Full-text available
One of the most important government applications of face recognition is the watchlist problem, where the goal is to identify a few people enlisted on a watchlist while ignoring the majority of innocent passersby. Since watchlists dynamically change and training times can be expensive, the deployed approaches use pre-trained deep networks only to p...
Article
Full-text available
As science attempts to close the gap between man and machine by building systems capable of learning, we must embrace the importance of the unknown. The ability to differentiate between known and unknown can be considered a critical element of any intelligent self-learning system. The ability to reject uncertain inputs has a very long history in ma...
Preprint
Full-text available
Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is not well defined for inputs far from a networks training set. In an uncontrolled environment, networks face many instances that are not of interest to them and have to be...
Conference Paper
Full-text available
Typically, most of the network intrusion detection systems use supervised learning techniques to identify network anomalies. A problem that exists is identifying the unknowns and update the classifiers with new query classes automatically. We define this as an open set incremental learning and propose to extend a recently introduced method, 'Extrem...
Conference Paper
Full-text available
Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveilla...
Article
Full-text available
Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems. While recent methods extract facial attributes using deep neural networks (DNNs) trained on labeled facial attribute data, the robustness of deep attribute representations has not been evaluated. In thi...
Conference Paper
Full-text available
Pattern recognition and machine learning research work often contains experimental results on real-world data, which corroborates hypotheses and provides a canvas for the development and comparison of new ideas. Results, in this context, are typically summarized as a set of tables and figures, allowing the comparison of various methods, highlightin...
Article
Full-text available
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem: they can unexpectedly and confidently misclassify examples formed by slightly perturbing otherwi...
Conference Paper
Full-text available
Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where on...
Conference Paper
Full-text available
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the bes...
Article
Full-text available
Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors. This unexpected lack of robustness raises fundamental questions about their generalization properties and poses a serious concern for practical deployments. As such perturbations can remain imp...
Article
Full-text available
In this paper, we investigate how the latest versions of deep convolutional neural networks perform on the facial attribute classification task. We test two loss functions to train the neural networks: the sigmoid cross-entropy loss usually used in multi-objective classification tasks, and the Euclidean loss normally applied to regression problems,...
Article
Full-text available
Deep neural networks have recently demonstrated excellent performance on various tasks. Despite recent advances, our understanding of these learning models is still incomplete, at least, as their unexpected vulnerability to imperceptibly small, non-random perturbations revealed. The existence of these so-called adversarial examples presents a serio...
Conference Paper
Full-text available
Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., training independent models for each task, for a growing set of problems joint optimization across all tasks has been shown to improve performance. We show that for deep convo...
Conference Paper
For applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomfort are desirable. Facial recognition is particularly appealing because it is minimally invasive yet offers relatively good recognition performance. Unfortunately, the co...
Article
Development of generic and autonomous anti-malware solutions is becoming increasingly vital as the deployment of stealth malware continues to increase at an alarming rate. In this paper, we survey malicious stealth technologies as well as existing autonomous countermeasures. Our findings suggest that while machine learning offers promising potentia...
Chapter
One important type of biometric authentication is face recognition , a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted toward unco...
Conference Paper
Full-text available
Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper we propose a simple yet effective solution for automatic facial attribute extraction...
Conference Paper
Full-text available
The paper addresses the problem of gender classification from face images. For feature extraction, we propose discrete Overlapping Block Patterns (OBP), which capture the characteristic structure from the image at various scales. Using integral images, these features can be computed in constant time. The feature extraction at multiple scales result...
Article
Full-text available
Graphs labeled with complex-valued Gabor jets are one of the important data formats for face recognition and the classification of facial images into medically relevant classes like genetic syndromes. We here present an interpolation rule and an iterative algorithm for the reconstruction of images from these graphs. This is especially important if...
Article
Full-text available
Graphs labeled with complex-valued Gabor jets are one of the important data formats for face recognition and the classification of facial images into medically relevant classes like genetic syndromes. We here present an interpolation rule and an iterative algorithm for the reconstruction of images from these graphs. This is especially important if...
Article
Full-text available
The locations of the eyes are the most commonly used features to perform face normalisation (i.e. alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this study, the authors study the sensitivity of open source implementations of five face recognition algorithms to misalignment caused by eye...
Article
Full-text available
An evaluation of the verification and calibration performance of a face recognition system based on inter-session variability modelling is presented. As an extension to calibration through linear transformation of scores, categorical calibration is introduced as a way to include additional information about images for calibration. The cost of likel...
Article
This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone....
Article
Objective: Cushing’s syndrome causes considerable harm to the body if left untreated, yet often remains undiagnosed for prolonged periods of time. In this study we aimed to test whether face classification software might help in discriminating patients with Cushing’s syndrome from healthy controls. Design: Diagnostic study. Patients: Using a regu...
Conference Paper
Full-text available
Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of thre...
Conference Paper
Full-text available
Bob is a free signal processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland. The toolbox is designed to meet the needs of researchers by reducing development time and efficiently processing data. Firstly, Bob provides a researcher-friendly Python environment for rapid developmen...
Conference Paper
Full-text available
In this paper we introduce the facereclib, the first software library that allows to compare a variety of face recognition algorithms on most of the known facial image databases and that permits rapid prototyping of novel ideas and testing of meta-parameters of face recognition algorithms. The facereclib is built on the open source signal processin...
Conference Paper
Full-text available
We analyze the relative relevance of Gabor amplitudes and phases for face recognition. We propose an algorithm to reliably estimate offset point disparities from phase differences and show that disparity-corrected Gabor phase differences are well suited for face recognition in difficult lighting conditions. The method reaches 74.8% recognition rate...
Article
Computer systems play an important role in clinical genetics and are a routine part of finding clinical diagnoses but make it difficult to fully exploit information derived from facial appearance. So far, automated syndrome diagnosis based on digital, facial photographs has been demonstrated under study conditions but has not been applied in clinic...
Article
Full-text available
Recent genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with non-syndromic cleft lip with or without cleft palate (NSCL/P), and other previous studies showed distinctly differing facial distance measurements when comparing unaffected relatives of NSCL/P patients with normal controls. Here, we test th...
Article
Full-text available
The delay between onset of first symptoms and diagnosis of the acromegaly is 6-10 yr. Acromegaly causes typical changes of the face that might be recognized by face classification software. The objective of the study was to assess classification accuracy of acromegaly by face-classification software. This was a diagnostic study. The study was condu...
Conference Paper
Full-text available
We briefly review the base techniques of elastic graph matching [1] and elastic bunch graph matching [2], which provide a method for face detection, matching, comparison, and identity decision. We then present a method that combines the advantages of Gabor-labeled graphs with maximum likelihood decision making. The improvements over pure bunch grap...
Article
Full-text available
We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions...

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