Eugen Gillich

Banknote authentication with mobile devices

Maintaining confidence in security documents, especially banknotes, is and remains a major concern for the central banks in order to maintain the stability of the economy around the world. In this paper we describe an image processing and pattern recognition approach which is based on the Sound-of-Intaglio principle for the usage in smart devices such as smartphones. Today, in many world regions smartphones are in use. These devices become more and more computing units, equipped with resource-limited, but effective CPUs, cameras with illumination, and flexible operating systems. Hence, it is obvious to apply smartphones for banknote authentication, especially for visually impaired persons. Our approach shows that those devices are capable of processing data under the constraints of image quality and processing power. Strictly a mobile device as such is not an industrial product for harsh environments, but it is possible to use mobile devices for banknote authentication. The concept is based on a new strategy for constructing adaptive Wavelets for the analysis of different print patterns on a banknote. Furthermore, a banknote specific feature vector is generated which describes an authentic banknote effectively under various illumination conditions. A multi-stage Lineardiscriminant- analysis classifier generates stable and reliable output.

Feature Extraction Algorithm for Banknote Textures Based on Incomplete Shift Invariant Wavelet Packet Transform

Segmentation and feature extraction algorithms based on Wavelet Transform or Wavelet Packet Transform are established in pattern recognition. Especially in the field of texture analysis they are known to be practical. One difficulty of texture analysis was in the past the characterization of different printing processes. In this paper we present a new algorithmic concept to feature extraction of textures, printed by different printing techniques, without the necessity of a previous teaching phase. The typical characters of distinct printed textures are extracted by first order statistical moments of wavelet coefficients. The algorithm uses the 2D incomplete shift invariant Wavelet Packet Transform, resulting in a fast execution time of O(N log2 (N )). Since the incomplete shift invariant Wavelet Packet Transform was exclusively defined for 1D-signals, it has been modified in this research. The application describes the detection of different printed security textures.

Advanced colour processing for mobile devices

Mobile devices such as smartphones are going to play an important role in professionally image processing tasks. However, mobile systems were not designed for such applications, especially in terms of image processing requirements like stability and robustness. One major drawback is the automatic white balance, which comes with the devices. It is necessary for many applications, but of no use when applied to shiny surfaces. Such an issue appears when image acquisition takes place in differently coloured illuminations caused by different environments. This results in inhomogeneous appearances of the same subject. In our paper we show a new approach for handling the complex task of generating a low-noise and sharp image without spatial filtering. Our method is based on the fact that we analyze the spectral and saturation distribution of the channels. Furthermore, the RGB space is transformed into a more convenient space, a particular HSI space. We generate the greyscale image by a control procedure that takes into account the colour channels. This leads in an adaptive colour mixing model with reduced noise. The results of the optimized images are used to show how, e. g., image classification benefits from our colour adaptation approach.

Stable image acquisition for mobile image processing applications

Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.

Real-time wavelet-based inline banknote-in-bundle counting for cut-and-bundle machines

Automatic banknote sheet cut-and-bundle machines are widely used within the scope of banknote production. Beside the cutting-and-bundling, which is a mature technology, image-processing-based quality inspection for this type of machine is attractive. We present in this work a new real-time Touchless Counting and perspective cutting blade quality insurance system, based on a Color-CCD-Camera and a dual-core Computer, for cut-and-bundle applications in banknote production. The system, which applies Wavelet-based multi-scale filtering is able to count banknotes inside a 100-bundle within 200-300 ms depending on the window size.