人体生物特征的综合分析与应用
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摘要
在日常生活的许多场合中,我们经常需要向别人明示自己的身份以便获得某种权利或者许可。例如,在登陆操作系统时,我们通过输入用户名和密码向计算机证明自己的身份,以便获得使用计算机的权利;在登机时,我们通过出示证件向机场安检人员证明自己的身份,以便获得登机的许可;在出入海关时,我们通过虹膜识别或者指纹识别向海关出入境管理人员证明自己的身份,以便获得出入境的许可,等等。因此,如何有效、方便、快捷地进行个人身份认证,保障人们的合法权益和各种社会活动的合法性和有效性,已经成为一个必须解决的关键社会问题。
身份认证就是通过各种技术或者非技术手段,向对方证明自己的身份,以便获得某种权利或者许可。传统的身份认证方式主要通过身份标识知识(如密码、口令等)和身份标识物品(如钥匙、身份证等)来证明身份,但由于主要借助体外物,一旦证明身份的标识知识(或标识物品)泄露(或被盗),其身份就容易被他人冒充(或取代)。因此,传统的身份认证方式给我们的生活带来了诸多不便和很多安全隐患,已经越来越难以满足社会的需求。
人的生物特征(如虹膜、视网膜、指纹等)由于具有唯一性、终生不变性、且不会被遗忘和丢失、不易伪造和被盗等优点,正成为身份认证的一个新介质,具有广阔的应用前景。基于生物特征的身份认证技术就是利用人体固有的生理特征(如人脸、虹膜、指纹等)和行为特征(如声音、签名、步态等)来进行个人身份的鉴定。常用的生物特征包括人脸、虹膜、视网膜、手形、指纹、声音、签名、步态等。利用上述提及的其中一个特征(模态),我们就可以构建单模态生物特征识别系统,而同时利用两种或多种特征(模态)则可以构建多模态生物特征识别系统。由于基于生物特征的身份认证方式具有传统身份认证方式无法比拟的优势,近年来已广泛应用于金融服务、人机交互、视频监控、信息安全、刑侦鉴定等领域。同时许多人都存在一个错误的观念,认为基于生物特征的身份认证技术已经相当成熟。然而事实恰恰相反,基于生物特征的身份认证技术中依然存在着许多问题等待解决,仍然是一个具有挑战性的重要的研究课题。例如:指纹识别技术中低质量指纹图像、小面积指纹图像和扭曲指纹图像的匹配问题,人脸识别技术中的开放问题(Open Problems),单模态生物特征识别技术中的小样本问题和冒充问题,等等。
由于基于生物特征的身份认证技术在实际应用中表现出的问题,本文针对一些关键问题进行了研究,主要包括低质量指纹图像的方向场估计问题、低质量人脸图像的超分辨率问题,单模态生物特征识别算法的通用性问题,多模态生物特征识别技术中的鲁棒性问题,具体研究内容和结果主要包括:
⑴提出基于主要脊和次要脊的指纹方向场估计方法。作为指纹的全局特征,指纹方向场在自动指纹识别系统中扮演了至关重要的角色。针对方向场估计算法存在估计精度不高、计算时间开销过大的现象,提出一种基于指纹块中主要脊和次要脊的指纹方向场估计方法。该方法主要包括4个处理步骤:预处理原始指纹图像,利用上半邻域搜索算法提取指纹前景块中的主要脊和次要脊,利用脊的直线模型估计指纹前景块方向,以及采用修正方案提高方向场估计的精度。本文方法适用于所有类型的指纹图像。实验结果表明,该方法不仅具有令人满意的估计精度,而且拥有很高的计算效率。更重要的是,算法还具有很强的鲁棒性,并且即使在低质量指纹数据库上,该算法也能够有效地提高指纹识别系统的性能。
⑵提出基于学习的两步互相关人脸图像超分辨率方法。人脸图像超分辨率是指从输入的低分辨率人脸图像合成出其高分辨率人脸图像。由于现有的基于学习的两步人脸超分辨率算法分开计算第一步的全局模型和第二步的局部模型,因此,计算时间开销大,并且合成的高分辨率人脸图像视觉效果差。为了克服这些问题,本文提出一种基于学习的两步互相关人脸图像超分辨率方法,该方法有效地建立了全局模型和局部模型之间的联系。在第一步(全局阶段)中,通过联合可操纵金字塔(Steerable Pyramid)的分解(Decomposition)和重建(Reconstruction)生成一幅全局高分辨率人脸图像;在第二步(残差补偿阶段)中,为了补偿全局高分辨率人脸图像的面部精细特征,在全局阶段求解得到的权重向量和候选样本的基础上,采用邻域重建算法来获取高分辨率残差人脸图像。最后,将高分辨率残差人脸图像叠加到全局高分辨率人脸图像上就得到了最终的高分辨率人脸图像。与现有算法相比,采用本文方法合成的高分辨率人脸图像更忠实于原始高分辨率人脸图像,并且在残差补偿阶段,由于利用全局阶段得到的权重向量和候选样本来计算高分辨率残差人脸图像,因此,极大地降低了计算的复杂度,同时又不会丢失面部精细特征。此外,本文方法还可以用在图像修复领域用来恢复人脸图像中破损区域,实验结果表明,即使人脸图像严重破损,应用本文方法仍可以恢复出具有良好视觉效果的图像。
⑶建立基于局部拓扑结构保存映射的统一的单模态生物特征识别系统。在传统的单模态生物特征识别系统中,针对不同的生物特征,具有特定的识别算法。因此,传统的单模态生物特征识别系统通用性能差。本文建立了一种统一的单模态生物特征识别系统,该系统具有良好的通用性,适用于人脸、掌纹、步态等绝大部分生物特征识别。本系统主要包括三个方面:(ⅰ)单位化原始生物特征数据;(ⅱ)利用局部拓扑结构保存映射(Local Topology Structure Preserving Projections,LTSPP)算法确定单位化生物特征数据的内蕴低维子空间;(ⅲ)在确定的低维子空间上利用类内距离和执行分类。针对该系统,本文还提出了LTSPP算法,该算法是一种新颖的子空间学习方法,它不仅考虑了类间信息,而且还充分利用了类内的局部拓扑结构信息。在表达不同类的分离性能上,LTSPP算法能将不同类的数据映射得尽可能远;同时,LTSPP算法通过利用线性重构系数保存了类内数据的局部拓扑结构。与其它子空间学习算法相比,LTSPP算法判别能力更强,更适合于生物特征识别。此外,对原始数据进行单位化处理以及在确定的低维子空间上利用类内距离和执行分类都能有效地提高生物特征识别系统的性能。在Yale人脸数据库和HumanID步态数据库上反复进行了识别实验,令人信服的实验结果表明:本文建立的统一的单模态生物特征识别系统是有效性的,并且,与其它的子空间学习算法相比,本文提出的LTSPP算法能产生更好的识别性能。
⑷提出基于贝叶斯层次模型的多模态生物性别识别方法。为了达到鲁棒性强、识别正确率高的性别识别性能,本文使用贝叶斯层次模型通过指纹模态和人脸模态来判断人的性别。与以往使用指纹的某种具体特征来进行性别识别的方法不同,本文使用“词袋模型”来分别进行指纹图像和人脸图像的特征表达。“词袋模型”是由一组视觉词构成的。在本文中,我们提出了一种新的监督方法来构造视觉词,使用该方法得到的视觉词消除了图像特征表达中冗余的特征维度,而加强了对性别分类有帮助的维度。进一步地,为了进行性别识别,我们分别将每种模态的图像特征表达嵌入产生式模型——贝叶斯层次模型中,并分别训练每种模态下的男性和女性的产生式模型。对任一给定的测试对象,我们分别估计其在不同模态下的性别。这种不同模态下的性别估计,是通过计算该模态下两种产生式模型的似然概率来实现的。最后,在决策层,通过融合不同模态下得到的性别估计来获得最终的识别结果。在实验室的大型指纹和人脸数据库上的实验结果表明,本文提出的新的图像特征表达以及产生式模型都是有效的,并且通过融合指纹和人脸信息而产生的更鲁棒的性别识别框架也是有效的。
On many occasions of daily life, we often need to express our own identity to others in order to obtain certain permissions. For example, when logging into the operating system, we prove our identity by entering a user name and password to obtain computer use privileges; when checking-in at the airport, we prove our identity by showing our ID to airport security officers in order to obtain the permission to board on a plane; when accessing to the customs, we prove our identity through iris recognition or fingerprint identification to customs officers in order to obtain immigration permits, etc. Therefore, how to perform personal identity authentication effectively, conveniently and quickly, protect people's legitimate rights and interests, and ensure legitimacy and effectiveness of various social activities have become a key social issue that must be resolved quickly.
Authentication is to prove our own identity to others in order to obtain certain rights through a variety of technical or non-technical means. Traditional authentication methods can be categorized as knowledge-based authentication (such as passwords) and device-based authentication (such as keys, ID). However, these methods mainly depend on external objects, once the password is leaked, whose identities may be pretended (or substituted) by others. Thus, the traditional authentication methods, which have brought a lot of inconveniences and potential security problems to our lives, become difficult to meet the needs of society.
Biological characteristics (such as iris, retina and fingerprint), due to their advantages such as unique, invariant, unable to be forgotten and lost, difficult to forge and steal, have become a new way to perform personal identity authentication with broad application prospects. Biometrics is a technology which is used to identify an individual based on the physiological (such as face, iris and fingerprint) or behavioral (such as speech, signature and gait) characteristics. Presently, there are several different characteristics which are widely used for the personal identification, including face, iris, retina, palmprint, fingerprint, speech, signature and gait. We can construct the unimodal biometric system by using one of these characteristics (modalities). Also, we can construct the multimodal biometric system by utilizing two or more individual characteristics (modalities). Since biometrics has incomparable advantages over traditional authentication methods, it has been widely used in financial services, human–computer interaction, video surveillance, information security, forensic identification and other fields in recent years. On the other hand, there is a misconception in many people’s mind that biometrics is already mature. On the contrary, there are many problems that still need to be resolved in biometrics, and biometrics is still a challenging and important research topic. For example: low-quality, small-area and distorted fingerprint matching problem in fingerprint identification; open problems in face recognition; small sample size problem and spoof attacks in unimodal biometric technology, and so on.
Since there are many unresolved problems in biometrics, this thesis has studied some key issues including orientation field estimation from low-quality fingerprint image, super-resolution from low-quality face image, popularity problem of unimodal biometric algorithm, robustness of multimodal biometric technology.
The major research contents and results of this thesis are as follows:
(1) We proposed a fingerprint orientation field estimation based on the primary and secondary ridges within the fingerprint block. As a global feature of fingerprints, the orientation field plays an important role in automatic fingerprint identification systems. Although many algorithms have been proposed for orientation field estimation, the results are not very satisfactory and the computational cost is expensive. In this paper, a novel algorithm based on the primary and secondary ridges within the fingerprint block is proposed for the orientation field estimation. The algorithm comprises four steps, preprocessing original fingerprint image, determining the primary and secondary ridges of fingerprint foreground block using the top semi-neighbor searching algorithm, estimating block direction based on straight-line model of such a primary ridge and correcting the spurious block directions. The proposed algorithm is suitable for almost all types of fingerprints. Experimental results show that it achieves satisfying estimation accuracy with high computational efficiency. A further experiment shows that it is more accurate and robust to noise compared with the previous works and can improve the performance of the fingerprint recognition system, even on low-quality fingerprint databases.
(2) We proposed a correlative two-step approach to hallucinating faces. Face hallucination is to synthesize high-resolution face image from the input low-resolution one. Although many two-step learning-based face hallucination approaches have been developed, they suffer from the expensive computational cost due to the separate calculating of the global and local models. To overcome this problem, we propose a correlative two-phase learning-based face hallucination approach which bridges a connection between global model and local model. In the first step (global phase), we build a global face hallucination framework by combining the steerable pyramid decomposition and the reconstruction. In the second step (residue compensation phase), based on the combination weights and constituent samples obtained in the global phase, a residue face image is synthesized by the neighbor reconstruction algorithm to compensate the hallucinated global face image with detailed facial features. The ultimate hallucinated face image is the composition of the global face image and the residue face image. Compared with existing approaches, in the global phase, our global face image is more similar to the original high-resolution face image. Moreover, in the residue compensation phase, we use combination weights and constituent samples obtained in the global phase to compute the residue face image, by which the computational complexity can be greatly reduced without compromising the quality of facial details. The experimental results and comparisons demonstrate that our approach can not only synthesize distinct high-resolution face images efficiently, but also has high computational efficiency. Furthermore, our proposed approach can be used to restore the damaged face images in image inpainting. The efficacy of our proposed approach is validated by recovering the seriously damaged face images with visually good results.
(3) We built a unimodal biometric system based on local topology structure preserving projections. In traditional unimodal biometric systems, they usually utilize specially designed recognition algorithms for different biological characteristics. Therefore, the traditional unimodal biometric systems have popularity problem. This paper proposes a unified unimodal biometric system that is suitable for most individual modalities, e.g., face, palmprint and gait. The proposed system consists of three steps: (i) preprocessing raw biometric data, (ii) determining the intrinsic low-dimensional subspace of preprocessed data by local topology structure preserving projections (LTSPP) and (iii) performing the classification in the determined subspace using the intra-class distance sum. In the proposed system, LTSPP is a novel subspace algorithm which focuses on not only the class information but also the local topology structure. In terms of representing the separability of different classes, LTSPP projects the inter-class margin data far apart. Meanwhile, LTSPP preserves the intra-class topology structures by using linear reconstruction coefficients. In comparison with other subspace algorithms, LTSPP possesses more discriminant abilities and is more suitable for biometric recognition. In addition, both preprocessing each raw datum into unit and performing the classification using the intra-class distance sum are helpful to improve the recognition rates. We carry out various recognition experiments using the Yale and HumanID gait databases. The encouraging experimental results demonstrate the effectiveness of our unified unimodal biometric system, and the proposed LTSPP algorithm for this system can yield the best recognition rates than the other algorithms.
(4) We proposed a multimodal gender recognition method using Bayesian hierarchical model. To achieve a robust and discriminative performance for gender recognition, we propose to estimate human gender from fingerprint and corresponding face information with Bayesian hierarchical model. Different from previous work of fingerprint based gender estimation that needs specially designed features, our bag-of-words model, composed of a set of visual words, is employed to structure fingerprint and face images representations respectively. In this model, we propose a novel supervised method to construct the visual words, by which the redundant feature dimensions are discarded and the important dimensions for gender classification are highlighted. In addition, such an image representation for each modality can be naturally embedded into a generative framework, Bayesian hierarchical model, for gender recognition purpose. For each modality, we train the generative models for both categories, male and female. By computing the likelihood of the two generative models, one can estimate the category label of this modality.We obtain the final recognition result by fusing different modalities at the decision level. Experiments on a large set of fingerprints and face database demonstrate the effectiveness of the proposed method of feature representation and new model. Complementary advantages from fingerprint-face fusion has benefited to our gender recognition.
身份认证就是通过各种技术或者非技术手段,向对方证明自己的身份,以便获得某种权利或者许可。传统的身份认证方式主要通过身份标识知识(如密码、口令等)和身份标识物品(如钥匙、身份证等)来证明身份,但由于主要借助体外物,一旦证明身份的标识知识(或标识物品)泄露(或被盗),其身份就容易被他人冒充(或取代)。因此,传统的身份认证方式给我们的生活带来了诸多不便和很多安全隐患,已经越来越难以满足社会的需求。
人的生物特征(如虹膜、视网膜、指纹等)由于具有唯一性、终生不变性、且不会被遗忘和丢失、不易伪造和被盗等优点,正成为身份认证的一个新介质,具有广阔的应用前景。基于生物特征的身份认证技术就是利用人体固有的生理特征(如人脸、虹膜、指纹等)和行为特征(如声音、签名、步态等)来进行个人身份的鉴定。常用的生物特征包括人脸、虹膜、视网膜、手形、指纹、声音、签名、步态等。利用上述提及的其中一个特征(模态),我们就可以构建单模态生物特征识别系统,而同时利用两种或多种特征(模态)则可以构建多模态生物特征识别系统。由于基于生物特征的身份认证方式具有传统身份认证方式无法比拟的优势,近年来已广泛应用于金融服务、人机交互、视频监控、信息安全、刑侦鉴定等领域。同时许多人都存在一个错误的观念,认为基于生物特征的身份认证技术已经相当成熟。然而事实恰恰相反,基于生物特征的身份认证技术中依然存在着许多问题等待解决,仍然是一个具有挑战性的重要的研究课题。例如:指纹识别技术中低质量指纹图像、小面积指纹图像和扭曲指纹图像的匹配问题,人脸识别技术中的开放问题(Open Problems),单模态生物特征识别技术中的小样本问题和冒充问题,等等。
由于基于生物特征的身份认证技术在实际应用中表现出的问题,本文针对一些关键问题进行了研究,主要包括低质量指纹图像的方向场估计问题、低质量人脸图像的超分辨率问题,单模态生物特征识别算法的通用性问题,多模态生物特征识别技术中的鲁棒性问题,具体研究内容和结果主要包括:
⑴提出基于主要脊和次要脊的指纹方向场估计方法。作为指纹的全局特征,指纹方向场在自动指纹识别系统中扮演了至关重要的角色。针对方向场估计算法存在估计精度不高、计算时间开销过大的现象,提出一种基于指纹块中主要脊和次要脊的指纹方向场估计方法。该方法主要包括4个处理步骤:预处理原始指纹图像,利用上半邻域搜索算法提取指纹前景块中的主要脊和次要脊,利用脊的直线模型估计指纹前景块方向,以及采用修正方案提高方向场估计的精度。本文方法适用于所有类型的指纹图像。实验结果表明,该方法不仅具有令人满意的估计精度,而且拥有很高的计算效率。更重要的是,算法还具有很强的鲁棒性,并且即使在低质量指纹数据库上,该算法也能够有效地提高指纹识别系统的性能。
⑵提出基于学习的两步互相关人脸图像超分辨率方法。人脸图像超分辨率是指从输入的低分辨率人脸图像合成出其高分辨率人脸图像。由于现有的基于学习的两步人脸超分辨率算法分开计算第一步的全局模型和第二步的局部模型,因此,计算时间开销大,并且合成的高分辨率人脸图像视觉效果差。为了克服这些问题,本文提出一种基于学习的两步互相关人脸图像超分辨率方法,该方法有效地建立了全局模型和局部模型之间的联系。在第一步(全局阶段)中,通过联合可操纵金字塔(Steerable Pyramid)的分解(Decomposition)和重建(Reconstruction)生成一幅全局高分辨率人脸图像;在第二步(残差补偿阶段)中,为了补偿全局高分辨率人脸图像的面部精细特征,在全局阶段求解得到的权重向量和候选样本的基础上,采用邻域重建算法来获取高分辨率残差人脸图像。最后,将高分辨率残差人脸图像叠加到全局高分辨率人脸图像上就得到了最终的高分辨率人脸图像。与现有算法相比,采用本文方法合成的高分辨率人脸图像更忠实于原始高分辨率人脸图像,并且在残差补偿阶段,由于利用全局阶段得到的权重向量和候选样本来计算高分辨率残差人脸图像,因此,极大地降低了计算的复杂度,同时又不会丢失面部精细特征。此外,本文方法还可以用在图像修复领域用来恢复人脸图像中破损区域,实验结果表明,即使人脸图像严重破损,应用本文方法仍可以恢复出具有良好视觉效果的图像。
⑶建立基于局部拓扑结构保存映射的统一的单模态生物特征识别系统。在传统的单模态生物特征识别系统中,针对不同的生物特征,具有特定的识别算法。因此,传统的单模态生物特征识别系统通用性能差。本文建立了一种统一的单模态生物特征识别系统,该系统具有良好的通用性,适用于人脸、掌纹、步态等绝大部分生物特征识别。本系统主要包括三个方面:(ⅰ)单位化原始生物特征数据;(ⅱ)利用局部拓扑结构保存映射(Local Topology Structure Preserving Projections,LTSPP)算法确定单位化生物特征数据的内蕴低维子空间;(ⅲ)在确定的低维子空间上利用类内距离和执行分类。针对该系统,本文还提出了LTSPP算法,该算法是一种新颖的子空间学习方法,它不仅考虑了类间信息,而且还充分利用了类内的局部拓扑结构信息。在表达不同类的分离性能上,LTSPP算法能将不同类的数据映射得尽可能远;同时,LTSPP算法通过利用线性重构系数保存了类内数据的局部拓扑结构。与其它子空间学习算法相比,LTSPP算法判别能力更强,更适合于生物特征识别。此外,对原始数据进行单位化处理以及在确定的低维子空间上利用类内距离和执行分类都能有效地提高生物特征识别系统的性能。在Yale人脸数据库和HumanID步态数据库上反复进行了识别实验,令人信服的实验结果表明:本文建立的统一的单模态生物特征识别系统是有效性的,并且,与其它的子空间学习算法相比,本文提出的LTSPP算法能产生更好的识别性能。
⑷提出基于贝叶斯层次模型的多模态生物性别识别方法。为了达到鲁棒性强、识别正确率高的性别识别性能,本文使用贝叶斯层次模型通过指纹模态和人脸模态来判断人的性别。与以往使用指纹的某种具体特征来进行性别识别的方法不同,本文使用“词袋模型”来分别进行指纹图像和人脸图像的特征表达。“词袋模型”是由一组视觉词构成的。在本文中,我们提出了一种新的监督方法来构造视觉词,使用该方法得到的视觉词消除了图像特征表达中冗余的特征维度,而加强了对性别分类有帮助的维度。进一步地,为了进行性别识别,我们分别将每种模态的图像特征表达嵌入产生式模型——贝叶斯层次模型中,并分别训练每种模态下的男性和女性的产生式模型。对任一给定的测试对象,我们分别估计其在不同模态下的性别。这种不同模态下的性别估计,是通过计算该模态下两种产生式模型的似然概率来实现的。最后,在决策层,通过融合不同模态下得到的性别估计来获得最终的识别结果。在实验室的大型指纹和人脸数据库上的实验结果表明,本文提出的新的图像特征表达以及产生式模型都是有效的,并且通过融合指纹和人脸信息而产生的更鲁棒的性别识别框架也是有效的。
On many occasions of daily life, we often need to express our own identity to others in order to obtain certain permissions. For example, when logging into the operating system, we prove our identity by entering a user name and password to obtain computer use privileges; when checking-in at the airport, we prove our identity by showing our ID to airport security officers in order to obtain the permission to board on a plane; when accessing to the customs, we prove our identity through iris recognition or fingerprint identification to customs officers in order to obtain immigration permits, etc. Therefore, how to perform personal identity authentication effectively, conveniently and quickly, protect people's legitimate rights and interests, and ensure legitimacy and effectiveness of various social activities have become a key social issue that must be resolved quickly.
Authentication is to prove our own identity to others in order to obtain certain rights through a variety of technical or non-technical means. Traditional authentication methods can be categorized as knowledge-based authentication (such as passwords) and device-based authentication (such as keys, ID). However, these methods mainly depend on external objects, once the password is leaked, whose identities may be pretended (or substituted) by others. Thus, the traditional authentication methods, which have brought a lot of inconveniences and potential security problems to our lives, become difficult to meet the needs of society.
Biological characteristics (such as iris, retina and fingerprint), due to their advantages such as unique, invariant, unable to be forgotten and lost, difficult to forge and steal, have become a new way to perform personal identity authentication with broad application prospects. Biometrics is a technology which is used to identify an individual based on the physiological (such as face, iris and fingerprint) or behavioral (such as speech, signature and gait) characteristics. Presently, there are several different characteristics which are widely used for the personal identification, including face, iris, retina, palmprint, fingerprint, speech, signature and gait. We can construct the unimodal biometric system by using one of these characteristics (modalities). Also, we can construct the multimodal biometric system by utilizing two or more individual characteristics (modalities). Since biometrics has incomparable advantages over traditional authentication methods, it has been widely used in financial services, human–computer interaction, video surveillance, information security, forensic identification and other fields in recent years. On the other hand, there is a misconception in many people’s mind that biometrics is already mature. On the contrary, there are many problems that still need to be resolved in biometrics, and biometrics is still a challenging and important research topic. For example: low-quality, small-area and distorted fingerprint matching problem in fingerprint identification; open problems in face recognition; small sample size problem and spoof attacks in unimodal biometric technology, and so on.
Since there are many unresolved problems in biometrics, this thesis has studied some key issues including orientation field estimation from low-quality fingerprint image, super-resolution from low-quality face image, popularity problem of unimodal biometric algorithm, robustness of multimodal biometric technology.
The major research contents and results of this thesis are as follows:
(1) We proposed a fingerprint orientation field estimation based on the primary and secondary ridges within the fingerprint block. As a global feature of fingerprints, the orientation field plays an important role in automatic fingerprint identification systems. Although many algorithms have been proposed for orientation field estimation, the results are not very satisfactory and the computational cost is expensive. In this paper, a novel algorithm based on the primary and secondary ridges within the fingerprint block is proposed for the orientation field estimation. The algorithm comprises four steps, preprocessing original fingerprint image, determining the primary and secondary ridges of fingerprint foreground block using the top semi-neighbor searching algorithm, estimating block direction based on straight-line model of such a primary ridge and correcting the spurious block directions. The proposed algorithm is suitable for almost all types of fingerprints. Experimental results show that it achieves satisfying estimation accuracy with high computational efficiency. A further experiment shows that it is more accurate and robust to noise compared with the previous works and can improve the performance of the fingerprint recognition system, even on low-quality fingerprint databases.
(2) We proposed a correlative two-step approach to hallucinating faces. Face hallucination is to synthesize high-resolution face image from the input low-resolution one. Although many two-step learning-based face hallucination approaches have been developed, they suffer from the expensive computational cost due to the separate calculating of the global and local models. To overcome this problem, we propose a correlative two-phase learning-based face hallucination approach which bridges a connection between global model and local model. In the first step (global phase), we build a global face hallucination framework by combining the steerable pyramid decomposition and the reconstruction. In the second step (residue compensation phase), based on the combination weights and constituent samples obtained in the global phase, a residue face image is synthesized by the neighbor reconstruction algorithm to compensate the hallucinated global face image with detailed facial features. The ultimate hallucinated face image is the composition of the global face image and the residue face image. Compared with existing approaches, in the global phase, our global face image is more similar to the original high-resolution face image. Moreover, in the residue compensation phase, we use combination weights and constituent samples obtained in the global phase to compute the residue face image, by which the computational complexity can be greatly reduced without compromising the quality of facial details. The experimental results and comparisons demonstrate that our approach can not only synthesize distinct high-resolution face images efficiently, but also has high computational efficiency. Furthermore, our proposed approach can be used to restore the damaged face images in image inpainting. The efficacy of our proposed approach is validated by recovering the seriously damaged face images with visually good results.
(3) We built a unimodal biometric system based on local topology structure preserving projections. In traditional unimodal biometric systems, they usually utilize specially designed recognition algorithms for different biological characteristics. Therefore, the traditional unimodal biometric systems have popularity problem. This paper proposes a unified unimodal biometric system that is suitable for most individual modalities, e.g., face, palmprint and gait. The proposed system consists of three steps: (i) preprocessing raw biometric data, (ii) determining the intrinsic low-dimensional subspace of preprocessed data by local topology structure preserving projections (LTSPP) and (iii) performing the classification in the determined subspace using the intra-class distance sum. In the proposed system, LTSPP is a novel subspace algorithm which focuses on not only the class information but also the local topology structure. In terms of representing the separability of different classes, LTSPP projects the inter-class margin data far apart. Meanwhile, LTSPP preserves the intra-class topology structures by using linear reconstruction coefficients. In comparison with other subspace algorithms, LTSPP possesses more discriminant abilities and is more suitable for biometric recognition. In addition, both preprocessing each raw datum into unit and performing the classification using the intra-class distance sum are helpful to improve the recognition rates. We carry out various recognition experiments using the Yale and HumanID gait databases. The encouraging experimental results demonstrate the effectiveness of our unified unimodal biometric system, and the proposed LTSPP algorithm for this system can yield the best recognition rates than the other algorithms.
(4) We proposed a multimodal gender recognition method using Bayesian hierarchical model. To achieve a robust and discriminative performance for gender recognition, we propose to estimate human gender from fingerprint and corresponding face information with Bayesian hierarchical model. Different from previous work of fingerprint based gender estimation that needs specially designed features, our bag-of-words model, composed of a set of visual words, is employed to structure fingerprint and face images representations respectively. In this model, we propose a novel supervised method to construct the visual words, by which the redundant feature dimensions are discarded and the important dimensions for gender classification are highlighted. In addition, such an image representation for each modality can be naturally embedded into a generative framework, Bayesian hierarchical model, for gender recognition purpose. For each modality, we train the generative models for both categories, male and female. By computing the likelihood of the two generative models, one can estimate the category label of this modality.We obtain the final recognition result by fusing different modalities at the decision level. Experiments on a large set of fingerprints and face database demonstrate the effectiveness of the proposed method of feature representation and new model. Complementary advantages from fingerprint-face fusion has benefited to our gender recognition.
引文
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