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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.

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.

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Finger vein has been proved to be an effective biometric for personal identification in recent years. Inspired by the good power of Gabor filter in capturing specific texture characteristics from any orientation of an image, this paper proposes a simple, yet powerful and efficient local descriptor for finger vein recognition, called histogram of competitive Gabor responses (HCGR). Specially, HCGR is based on a set of competitive Gabor response (CGR) which consists of two components: competitive Gabor magnitude (CGM) and competitive Gabor orientation (CGO). A set of CGR includes the information on magnitude and orientation of the maximum responses of the Gabor filter bank with a number of different orientations. For a given image, we calculate its CGM image and CGO image and represent them in a concatenated histogram, called HCGR. This histogram can efficiently and effectively exploit the discriminative orientation and local features in a finger vein image. The experimental results obtained on our publically available finger vein image database MMCBNU_6000 demonstrate that the proposed HCGR outperforms the classical local operators such as Gabor, steerable, histogram of oriented gradients (HOG) and local binary pattern (LBP).

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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.