Lu Leng 교수

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successful

Although Discrete Cosine Transform (DCT) is widely employed to extract proper features for biometric recognition, the problem on how to select proper DCT coefficients to obtain the best discrimination effect has not been solved satisfactorily. Some approaches discard the low-frequency DCT coefficients unreasonably and rely on proper premasking window to improve performance. But there is not a uniform criterion to optimize the shape and size of the premasking window, so it is an inconvenient proc

Discrimination power analysis (DPA) is a statistical analysis combining discrimination concept with discrete cosine transform coefficients (DCTCs) properties. Unfortunately there is not a uniform and effective criterion to optimize the shape and size of premasking window on which the effect of DPA excessively relies. Proper premasking is an auxiliary process to select the feature coefficients that have more discrimination power (DP). Dynamic weighted DPA (DWDPA) is proposed in this paper to enha

Multi-modal biometrics enjoy more merits than their single-modal counterparts in terms of accuracy performance and security. The fusion of hand-based biometrics such as palmprint and palmvein is straightforward since they can be acquired simultaneously with a customized image acquisition device. Conjugate 2DPalmHash Code (CTDPHC), which is constructed by 2DPalmHash Codes (2DPHCs) of palmprint and palmvein, is proposed as a cancelable multi-modal biometric. To determine the proper fusion strategy

ABSTRACT Biometric template security and privacy issues are critical in biometric authentication systems and require special attention. However, remote biometric authentication systems demand wider array of measures for maximum protection. This paper proposes a remote cancelable palmprint authentication protocol based on multi‐directional two‐dimensional PalmPhasor‐fusion. The main contribution is three‐fold. First, with a transposition direction selection mechanism, multi‐directional two‐dimens

Most existing cancelable biometric frameworks are based on one-dimensional (ID) vectors rather than two-dimensional (2D) images or feature matrices. 2D cancelable biometrics, generated directly from images of feature matrices, were proposed based on two-directional two-dimensional fusion sparse random projection ((2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FSRP) and two-directional two-dimensional plus sparse random projection ((2D)

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Than

Biometric-based authentication is widely deployed on multimedia systems currently; however, biometric systems are vulnerable to image-level attacks for impersonation. Reconstruction attack (RA) and presentation attack (PA) are two typical instances for image-level attacks. In RA, the reconstructed images often have insufficient naturalness due to the presence of remarkable counterfeit appearance, thus their forgeries can be easily detected by machine or human. The PA requires genuine users’ orig