Chris Stein

Bio: Chris Stein is an academic researcher from Darmstadt University of Applied Sciences. The author has contributed to research in topics: Video tracking. The author has an hindex of 2, co-authored 3 publications receiving 89 citations.

Fingerphoto recognition with smartphone cameras

Abstract: This paper is concerned with the authentication of people on smartphones using fingerphoto recognition. In this work, fingerphotos are captured with the built-in camera of the smartphone. The proposed authentication method is analyzed for feasibility and implemented in a prototype as application for the Android operating system. Algorithms for the capture process are developed to ensure a minimum of quality of the captured photos to enable a reliable fingerphoto recognition. Several methods for preprocessing of the captured samples are analyzed and performant solutions to evaluate the photos are developed to enhance the recognition rates. This is achieved by evaluating a wide range of different parameters and configurations of the algorithms as well as various combinations of preprocessing chains for the captured samples. The operations for preprocessing are selected with respect to their computational effort to guarantee that they can be executed on a smartphone with limited computation and memory capacity. The developed prototype is evaluated in user tests with two different smartphones. Additionally, a biometric database containing photos of the two test devices from 41 test subjects is created. These fingerphotos are used to evaluate and optimize the procedures.

Video-based fingerphoto recognition with anti-spoofing techniques with smartphone cameras

Abstract: This work is concerned with the acquisition of fingerprints samples on smartphones with the built-in smartphone camera. A novel approach to capture multiple fingerphotos in a videostream with a smartphone camera and the processing of the photos for the finger recognition is discussed in this paper. The proposed technique offers a convenient and efficient way to capture multiple samples of a biometric instance in a short time frame. Due the fact that fingerphotos can be easily replicated with low effort (e.g. print outs with an ordinary printer) and thus are vulnerable to presentation attacks, anti-spoofing algorithms were developed to detect such spoof attempts. The algorithms for the detection and segmentation of the finger as well the preprocessing of the photo with graphical operations and anti-spoofing were implemented in a prototype as application for the Android operating system. User tests are performed to evaluate the usability and to create a database of biometric samples for offline evaluation of the recognition performance. Further tests are done with diverse artefacts such as printed finger images, fake fingers of gelatin, gummy and silicon as well finger replay videos to measure the resistance of the developed solution against presentation attacks.

Eignung von Smartphone-Kameras zur Fingerabdruckerkennung und Methoden zur Verbesserung der Qualität der Fingerbilder

Abstract: Diese Arbeit befasst sich mit der Untersuchung auf die Eignung von SmartphoneKameras fur eine Fingerabdruckerkennung. Hierzu werden unter Alltagsbedingungen Fingerfotos mit den Kameras von verschiedenen Smartphones mit mehreren Personen gemacht und ausgewertet. Vier der funf untersuchten Gerate stellen sich bei der Aufnahme von Fingerfotos als untauglich dar. Die Aufnahmen des tauglichen Gerates werden weiter auf ihre Qualitat untersucht und mit welchen Methoden diese erhoht werden kann, um die Erkennungsleistung zu verbessern.

EchoPrint: Two-factor Authentication using Acoustics and Vision on Smartphones

Abstract: User authentication on smartphones must satisfy both security and convenience, an inherently difficult balancing art. Apple's FaceID is arguably the latest of such efforts, at the cost of additional hardware (e.g., dot projector, flood illuminator and infrared camera). We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a Convolutional Neural Network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial landmark locations to feed a binary Support Vector Machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show that EchoPrint achieves 93.75% balanced accuracy and 93.50% F-score, while the average precision is 98.05%, and no image/video based attack is observed to succeed in spoofing.

Fingerphoto recognition with smartphone cameras

Abstract: This paper is concerned with the authentication of people on smartphones using fingerphoto recognition. In this work, fingerphotos are captured with the built-in camera of the smartphone. The proposed authentication method is analyzed for feasibility and implemented in a prototype as application for the Android operating system. Algorithms for the capture process are developed to ensure a minimum of quality of the captured photos to enable a reliable fingerphoto recognition. Several methods for preprocessing of the captured samples are analyzed and performant solutions to evaluate the photos are developed to enhance the recognition rates. This is achieved by evaluating a wide range of different parameters and configurations of the algorithms as well as various combinations of preprocessing chains for the captured samples. The operations for preprocessing are selected with respect to their computational effort to guarantee that they can be executed on a smartphone with limited computation and memory capacity. The developed prototype is evaluated in user tests with two different smartphones. Additionally, a biometric database containing photos of the two test devices from 41 test subjects is created. These fingerphotos are used to evaluate and optimize the procedures.

On smartphone camera based fingerphoto authentication

Abstract: Authenticating fingerphoto images captured using a smartphone camera, provide a good alternate solution in place of traditional pin or pattern based approaches. There are multiple challenges associated with fingerphoto authentication such as background variations, environmental illumination, estimating finger position, and camera resolution. In this research, we propose a novel ScatNet feature based fingerphoto matching approach. Effective fingerphoto segmentation and enhancement are performed to aid the matching process and to attenuate the effect of capture variations. Further, we propose and create a publicly available smartphone fingerphoto database having three different subsets addressing the challenges of environmental illumination and background, along with their corresponding live scan fingerprints. Experimental results show improved performance across multiple challenges present in the database.

Scaling-robust fingerprint verification with smartphone camera in real-life scenarios

Abstract: We propose a new scheme for accurate contactless fingerprint recognition captured with smartphone cameras under various real-life scenarios. The proposed scheme can be structured using three building blocks namely: (1) finger segmentation (2) pre-processing and scaling (3) minutiae extraction and comparison. The proposed finger segmentation scheme is based on Mean Shift Segmentation (MSS) algorithm followed by multiple metrics to accurately segment the finger from the background. We then propose a new scheme to perform the finger scaling to accurately extract the fingerprint region from the segmented finger. Finally, the comparison is carried out based on the minutiae features extracted from the scaled fingerprint images. Extensive experiments are carried out on our recently collected contactless fingerprint dataset consisting of 1800 samples from 25 subjects. In order to effectively evaluate the robustness of the proposed scheme, the whole dataset is constructed using three different smartphone's namely: Nokia N8, iPhone 4 and Samsung S1. The experimental results have shown the effectiveness of the proposed scheme on various complex backgrounds with an Equal Error Rate of 3.74% noted on Samsung S1 smartphone camera.