基于混合技术的EMR系统医学信息保护

随着电子病历记录(EMR)系统在诸多医院的广泛使用,医生可以利用计算机云交互和共享医学数据,并通过下一代临床决策支持系统提取医学记录信息,从而做出精确诊断。为解决EMR系统传输过程中的医学信息泄露或纂改问题,将EMR系统中经过B++编码后的医学数据嵌入到病人的指纹中,通过基于非下采样Contourlet变换的数字水印算法,提取含有特征结构信息和注册密钥的指纹,并将其嵌入到病人的医学图像中,提高EMR系统医学信息的安全性。实验结果表明,该方法实现的医学水印图像具有较高的鲁棒性,并且由于允许高容量的医学信息嵌入,保证了医学数据的完整性。     

基于惩罚系数的人脸和声音融合识别方法

不同的生物特征采集装置采集的样本品质各不相同,样本品质高的识别可靠性较高,同时对于同一个样本,采用不同的识别方法,好的识别方法识别可靠性也较高,因而本文提出一种利用生物特征的样本品质和识别专家可靠性的融合识别（QSVM）方法。首先根据样本品质和识别专家可靠性得到样本惩罚系数和可靠性惩罚系数,进而得到总体惩罚系数,最后利用总体惩罚系数对支持向量机识别算法进行修改。本文采用XM2VTS数据库,分别将QSVM方法与贝叶斯分类法、Fisher线性判别函数分类法、多层感知器分类法和平均融合方法、SVM方法的半和误差率（HTER）进行比较,实验结果表明QSVM方法的半和误差率较小。     

基于非高斯二维Gabor滤波器的生物特征提取算法

Gabor滤波器是一种非常有效的图像纹理特征提取算子。Gabor滤波器可以看作是高斯核函数在频域由复正弦函数调制而成,其频谱仍是高斯函数。采用Gabor滤波器对图像进行滤波处理时,图像所包含的位于高斯函数的频带范围之外的非高斯频谱上的重要信息并不能被Gabor滤波器所提取。提出另外一种二维非高斯Gabor滤波器用于生物特征提取。在所提二维非高斯Gabor滤波器中引入了一个新的参数。这个新参数可以控制二维非高斯Gabor滤波器包络的形状。为了证明所提出的非高斯Gabor滤波器的优越性,在人脸和掌纹数据库中做了大量的实验。实验结果表明,提出的二维非高斯Gabor滤波器的性能相比于传统二维Gabor滤波器有较大的提高。     

双生物识别图象的光学加密方法研究

本文提出了一种将两幅生物识别图象通过一套光学系统进行同时加密的方法。该方法先将一幅图象转换为纯相位和进行纯相位编码,再对另一幅图象编码,然后经过4f光学系统作频率域纯相位编码,并利用与编码过程类似的方法进行解码。可以证明该方法的编码图象为恒定的白噪声。解码过程具有鲁棒性,相位部分图象加密的安全性要高于振幅部分图象的加密。并分析了加性高斯噪声对解码图象的影响     

序列虹膜图像质量的评价方法

提出一种使用二维图像复杂度和傅里叶频谱值作为评价因子、利用分类器进行序列虹膜图像质量评价的方法.首先利用投影和图像二值化方法得到虹膜图像评价区域,然后计算该区域的图像二维复杂度和傅里叶频谱值,以此作为特征并使用分类器选择最清晰的一幅图像用于身份认证.实验表明,在K-NN,BP神经网络和支持向量机等多种分类器中使用上述评价因子的结果均比较理想,可以挑选出符合要求的虹膜图像.     

Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

Electrocardiogram (ECG) biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning (SRL) and deep neural network,robust identification for small-scale data is still a challenge.To address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest (MDCBF) model for ECG biometric recognition.We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise.Then we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade coding.In the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low noise.In deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative features.Extensive experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.     

NeuroBiometric: An Eye Blink Based Biometric Authentication System Using an Event-Based Neuromorphic Vision Sensor

The rise of the Internet and identity authentication systems has brought convenience to people’s lives but has also introduced the potential risk of privacy leaks. Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data. This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution. The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur, which leads to advantageous characteristics such as an ultra-low latency response. We first propose a set of effective biometric features describing the motion, speed, energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities. We then train the ensemble model and non-ensemble model with our NeuroBiometric dataset for biometrics authentication. The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925. The low false positive rates (about 0.002) and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a user’s appearance. The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.      

On the implementation of IP protection using biometrics based information hiding and firewall

System-on-chip-based design style creates a revolution in very large scale integration industry with design efficiency, operating speed and development time. To support this process, reuse and exchange of components are essential in electronic form called intellectual property (IP). This, however, increases the possibility of encroachment of IP of the design. So copyright protection of IP against piracy is the most important concern for IP vendors. The existing solutions for IP protection are still not secure enough with flexibility, cost, etc. This paper proposes an information-hiding-based solution for IP protection by embedding a biometric copyright information and firewall inside an IP in the form of a finite state machine with unique configuration. The scheme first introduces biometric signature-based copyright as ownership proof. Second, firewall interrupts the normal functionality of IP at the end of the user time period. The experimental outcomes of field-programmable-gate-array implementation illustrate the efficiency of the proposed method.     

A WEB SERVICE-BASED MOBILE APPLICATION FOR DETECTING KAYSER-FLEISCHER RING IN EYE CORNEA

A Kayser-Fleischer ring is a visible golden-brown, sometimes orange or greyish, pigmentation in the cornea. This is a common symptom of a rare genetic disorder known as Wilson Disease. This pathology is cause of a malfunction in the copper excretion from the organism. Copper accumulates in tissues and consequently is responsible of oxidative processes in the organs affected. Kayser-Fleischer ring is a direct consequence of the copper deposit in the cornea. It is considered a basic diagnostic sign of Wilson disease in individuals with neurological disorders due to copper accumulation in brain. However, few ophthalmologists are experienced in identifying Kayser-Fleischer rings. In this article, a mobile application is proposed to detect automatically the Kayser-Fleischer ring. The smartphone takes a photograph of patient's eye and sends it to a webservice. The latter performs the image processing by using a segmentation algorithm. Once a Kayser-Fleischer ring has been detected, biometric measurements provide the percentage extent of the corneal region affected by the symptom. This parameter allows the system to obtain information on the severity of the oxidative process as an indirect measure of the pathology stage. Subsequently, further medical examinations support the physician to diagnose the Wilson disease. The proposed diagnostic measurement system is non-invasive and fully automated. At the moment the detection of the Kayser-Fleischer ring is made by an ophthalmologist with a visual examination. However, the symptoms are not always recognized, and therefore interpretation errors and false-negative diagnoses are possible. In this article, a proposed diagnostic tool is described to improve the accuracy of methods used in practice and to resolve the physician's suspicion about the pathology occurrence.     

Visual cryptography: A brief survey

Visual cryptography is an emerging technology to address the concerns regarding privacy of images. It is a powerful technique combining both the impeccable ciphers and secret sharing in cryptography with that of the raster graphics. Visual cryptography divides the secret image into shares or shadows during encryption. The term “visual” in visual cryptography stands for the fact that during decryption phase, a user can perceive the recovered secret with his/her visual system, without the intervention of machines. Various visual cryptography techniques have been discussed extensively in this survey. The metrics used to analyse the effectiveness of visual cryptography techniques have been briefed. The significant applications of visual cryptography have also been summarized in the survey.