This paper describes an embedded multi-user login system based on fingerprint recognition. The system, built using the Sitsang development board and embedded Linux, implements all fingerprint acquisition, preprocessing, minutia extraction, match, identification, user registration, and template encryption on the board. By careful analysis of the accuracy requirement as well as the arithmetic precision to be used, we optimized the algorithms so that the whole system can work in real-time in the embedded environment based on Intel(R) PXA255 processor. The fingerprint verification, which is the core part of the system, is fully tested on a fingerprint database consists of 1149 fingerprint images. The result shows that we can achieve an accuracy of more than 95%. Field testing of 20 registered users has further proved the reliability of our system. The core part of our system, then embedded fingerprint authentication, can also be applied in many different embedded applications concerning security problems.
This paper presents an experimental study of the implementation of a face authentication system for mobile devices. Our system is based on a widely adopted face recognition technique called Principal Component Analysis (PCA). The execution time of the baseline system on a PDA is unacceptably slow -- a typical authentication session takes more than 30 seconds. To make real-time face authentication possible on mobile devices, optimization is needed. In our study, extensive profiling is done to pinpoint the execution hotspots in the system. Based on the profiling results, our optimization strategy focused on replacing the large amount of slow floating point calculations with their fixed-point versions. Range estimation is also carried out to determine the range of floating point values that must be accommodated by the final, fixed-point version of our system. Compared with the baseline system, experimental results indicate that our optimized system runs as much as 47 times faster for PCA projection. Using the optimized system, a complete authentication session takes only 5 seconds. Real time face authentication for mobile device is achieved with no significant loss in recognition accuracy.
The importance of digital maps increases continuously. Unfortunately, it is costly and time consuming to create a digital map out of the void. If we can successfully vectorize scanned paper maps, digital map production will be much easier and previous map resources can be well utilized. The first step of map vectorization is color clustering. Currently there exist a number of fast and efficient algorithms for automatic color clustering. However, they may not work well for maps of enormous noises. Here we introduce two interactive color clustering algorithms: color clustering with pre-calculated index colors (PCIC) and color clustering with pre-calculated color ranges (PCCR). A number of experiments are conducted to investigate into the performance of the two approaches. Finally, we introduce a novel and efficient vectorization algorithm, which can perform the entire vectorization algorithm in one pass of linear time.
The solid state fingerprint sensors are small in size and can be easily installed on mobile devices. However, the small contact area limits the number of collected minutiae, making the fingerprint matching less reliable. Recently, template synthesis of fingerprints is proposed to augment the available minutiae set during registration. However, this approach is not feasible when two fingerprints are severely distorted. In this paper, we propose a novel way of fingerprint template normalization for distortion removal. Instead of performing expensive processing of the fingerprint images, we suggest that the normalization can be applied to the extracted minutiae using the ridge structure gathered during direct gray scale.
The demand for digital maps continues to arise as mobile electronic devices become more popular nowadays. Instead of creating the entire map from void, we may convert a scanned paper map into a digital one. Color clustering is the very first step of the conversion process. Currently, most of the existing clustering algorithms are fully automatic. They are fast and efficient but may not work well in map conversion because of the numerous ambiguous issues associated with printed maps. Here we introduce two interactive approaches for color clustering on the map: color clustering with pre-calculated index colors (PCIC) and color clustering with pre-calculated color ranges (PCCR). We also introduce a memory model that could enhance and integrate different image processing techniques for fine-tuning the clustering results. Problems and examples of the algorithms are discussed in the paper.