Shan Juan Xie

Robust Finger Vein ROI Localization Based on Flexible Segmentation

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.

Fingerprint matching based on extreme learning machine

Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.

Guided Gabor Filter for Finger Vein Pattern Extraction

In this paper, a novel explicit image filter, called Guided Gabor filter, is proposed to extract the finger vein pattern without any segmentation processing, and lower performance reduction for poor quality images which result from low contrast, illumination, or noise effects, etc. The proposed filter is contributed for finger vein enhancement, noise reduction, and haze removal without being affected by the brightness of the vein. It performs well not only on ridge detection like the Gabor filter, but on image enhancement as an edge-preserving smoothing operator without the gradient reversal artifacts. The experimental results show that the proposed method is able to get vein pattern more clearly and faster than the existing methods, and improve the matching performance with higher recognition rate.

Intelligent fingerprint quality analysis using online sequential extreme learning machine

Because the quality of fingerprints can be degraded by diverse factors, recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint authentication systems. This paper proposes an effective fingerprint quality analysis approach based on the online sequential extreme learning machine (OS-ELM). The proposed method is based not only on basic fingerprint properties, but also on the physical properties of the various sensors. Instead of splitting a fingerprint image into traditional small blocks, direction-based segmentation using the Gabor filter is used. From the segmented image, a feature set which consists of four selected independent local or global features: orientation certainty, local orientation quality, consistency, and ridge distance, is extracted. The selected feature set is robust against various factors responsible for quality degradation and can satisfy the requirements of different types of capture sensors. With the contribution of the OS-ELM classifier, the extracted feature set is used to determine whether or not a fingerprint image should be accepted as an input to the recognition system. Experimental results show that the proposed method performs better in terms of accuracy and time consumed than BPNN-based and SVM-based methods. An obvious improvement to the fingerprint recognition system is achieved by adding a quality analysis system. Other comparisons to traditional methods also show that the proposed method outperforms others.

An Optimal Orientation Certainty Level Approach for Fingerprint Quality Estimation

Analyzing the quality of fingerprints in advance can be benefit for a fingerprint recognition system to improve its performance. Representative features for the quality assessment of fingerprint images from two existed types of capture devices are different. Orientation certainty level (OCL) is an effective method to extract image orientation feature. However it is not an effective estimation system to cooperate with the extracted features. In this paper, we explore the application of optimization theory, and support vector machine (SVM) in the field of image processing. Our proposed optimal orientation certainty level (OOCL) approach calculates the OCL for each block, extracts features from the optimal OCL system and uses the SVM classifier to determine whether an image should be accepted as an input to the recognition system. Experimental results show that the proposed OOCL method can improve the recognition rate than OCL method.

Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.

Effective Fingerprint Quality Estimation for Diverse Capture Sensors

Recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint recognition systems. The representative features to assess the quality of fingerprint images from different types of capture sensors are known to vary. In this paper, an effective quality estimation system that can be adapted for different types of capture sensors is designed by modifying and combining a set of features including orientation certainty, local orientation quality and consistency. The proposed system extracts basic features, and generates next level features which are applicable for various types of capture sensors. The system then uses the Support Vector Machine (SVM) classifier to determine whether or not an image should be accepted as input to the recognition system. The experimental results show that the proposed method can perform better than previous methods in terms of accuracy. In the meanwhile, the proposed method has an ability to eliminate residue images from the optical and capacitive sensors, and the coarse images from thermal sensors.

Fingerprint reference point detemination based on a novel ridgeline feature

Different with a traditional orientation analysis or frequency analysis which is commonly used for the fingerprint reference point determination, a new fingerprint reference point determination method based on a novel inconsistency feature obtained from the relationship between ridgelines and curves of a fingerprint is proposed. For the proposed method, an improved enhancement method is introduced and the posterior probability theory is used to determine the reference point in pixel precision. Experimental results demonstrate its feasibility, validity and ability independent of the quality and types of fingerprints.

Rule-Based Fingerprint Quality Estimation System Using the Optimal Orientation Certainty Level Approach

The fingerprint quality can be used as a good predictor for fingerprint recognition performance. Knowing the fingerprint quality in advance is useful to improve the performance of fingerprint recognition system. In this paper, we propose an effective quality estimation system with 4 rules which are applied in two-step. Each rule consists of a Back-Propagation Neural Networks (BPNN) classifier based on Optimized Orientation Certainty Level (OOCL) features extracted locally from fingerprint images. Experimental results show that the proposed two-step OOCL method can estimate fingerprint quality more effectively.