Miao Qi

A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints

Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain image. Fuzzy c-means (FCM) clustering algorithm has been widely used in many medical image segmentations. However, the conventionally standard FCM algorithm is sensitive to noise because of not taking into account the spatial information. To overcome the above problem, a modified FCM algorithm (called mFCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results not only on synthesized image but also MRI brain image which degraded by Gaussian noise and salt-pepper noise demonstrates that the presented algorithm performs more robust to noise than the standard FCM algorithm.

A novel image hiding approach based on correlation analysis for secure multimodal biometrics

This paper proposes a novel multimodal biometric images hiding approach based on correlation analysis, which is used to protect the security and integrity of transmitted multimodal biometric images for network-based identification. Compared with existing methods, the correlation between the biometric images and the cover image is first analyzed by partial least squares (PLS) and particle swarm optimization (PSO), aiming to make use of the abundant information of cover image to represent the biometric images. Representing the biometric images using the corresponding content of cover image results in the generation of the residual images with much less energy. Then, considering the human visual system (HVS) model, the residual images as the secret images are embedded into the cover image using middle-significant-bit (MSB) method. Extensive experimental results demonstrate that the proposed approach not only provides good imperceptibility but also resists some common attacks and assures the effectiveness of network-based multimodal biometrics identification.

An adaptively weighted sub-pattern locality preserving projection for face recognition

In this paper, an adaptively weighted sub-pattern locality preserving projection (Aw-SpLPP) algorithm is proposed for face recognition. Unlike the traditional LPP algorithm which operates directly on the whole face image patterns and obtains a global face features that best detects the essential face manifold structure, the proposed Aw-SpLPP method operates on sub-patterns partitioned from an original whole face image and separately extracts corresponding local sub-features from them. Furthermore, the contribution of each sub-pattern can be adaptively computed by Aw-SpLPP in order to enhance the robustness to facial pose, expression and illumination variations. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, YaleB and PIE). Experimental results show that Aw-SpLPP outperforms other holistic and sub-pattern based methods.

User-Specific Iris Authentication Based on Feature Selection

A novel iris localization method and user-specific automatic iris authentication approach based on feature selection is proposed in this paper. First, two iris sub-regions, where are nearly not occluded by useless parts such as eyelash and eyelid, are segmented as region of interest (ROI). Second, multi-scale Gabor filters are adopted to extract the texture feature of ROI. Third, genetic algorithm (GA) and support vector machine (SVM) are employed for feature selection and classification. Through feature selection, each user has specific feature index and authentication modality. For proving the effectiveness and feasibility, we compare the proposed specific feature selection approach with the method without feature selection on a small database. The experimental results show the proposed approach can achieve lower error rates in iris authentication.

A two stage neural network-based personal identification system using handprint

With the increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decades. Handprint identification, as an emerging biometric identification technology, is receiving more and more attention in both research and practical applications as time goes by. In this paper, a novel approach for handprint identification is proposed. Firstly, region of interest is segmented through hands key points localization, then the Gabor filtering and Zernike moments methods are used to extract the palmprint features. A two stage neural network structure is employed to measure the degree of similarity in the identification stage. The experimental results demonstrate that the proposed approach is effective and feasible.

A structure-preserved local matching approach for face recognition

In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.

Prediction of Microporous Aluminophosphate AlPO4‐5 Based on Resampling Using Partial Least Squares and Logistic Discrimination

In this paper, Partial Least Squares (PLS) regression and Logistic Discrimination (LD) are employed to predict the formation of microporous aluminophosphate AlPO4‐5 based on the database of AlPO synthesis, which aims to provide a useful guidance to the rational synthesis of microporous materials as well as other inorganic crystalline materials. To deal with the problem of class imbalance, four guided resampling methods considering not only the between‐class imbalance but also the within‐class imbalance are proposed. Experimental results indicate that the presented methods are competent for predicting the formation of microporous aluminophosphate AlPO4‐5. Specially, compared with some existing resampling methods, our proposed resampling methods exhibit much better predictive results.

Linear discriminant projection embedding based on patches alignment

Dimensionality reduction is often required as a preliminary stage in many data analysis applications. In this paper, we propose a novel supervised dimensionality reduction method, called linear discriminant projection embedding (LDPE), for pattern recognition. LDPE first chooses a set of overlapping patches which cover all data points using a minimum set cover algorithm with geodesic distance constraint. Then, principal component analysis (PCA) is applied on each patch to obtain the datas local representations. Finally, patches alignment technique combined with modified maximum margin criterion (MMC) is used to yield the discriminant global embedding. LDPE takes both label information and structure of manifold into account, thus it can maximize the dissimilarities between different classes and preserve datas intrinsic structures simultaneously. The efficiency of the proposed algorithm is demonstrated by extensive experiments using three standard face databases (ORL, YALE and CMU PIE). Experimental results show that LDPE outperforms other classical and state of art algorithms.

Iris verification using wavelet moments and neural network

In this paper, a novel and robust verification approach using iris features is presented. Contrasting with conventional approaches, only two iris subregions instead of entire iris, where are nearly not occluded by useless parts such as eyelash and eyelid, are segmented for verification. Gabor filtering and wavelet moments methods are used to extract the iris texture features. In the verification stage, the principal component analysis (PCA) technique and one-class-one-network (Back-Propagation Neural Network (BPNN)) classification structure are employed for dimensionality reduction and classification, respectively. The experimental results show that the correct verification rate can reach 98.65% using our proposed approach.

Locality Constrained Joint Dynamic Sparse Representation for Local Matching Based Face Recognition

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.