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In this paper, a new approach for fingerprint ridge orientation field matching based on a novel HMM (Hidden Markov Model) is proposed. The proposed method comprises several steps. First steps are devoted to regular fingerprint preprocesses and ridge orientation estimation. Then, the fingerprint images are registered along a reference point. Next, the proposed HMM topology is applied to the predetermined fingerprint orientation field information around the reference point. The suggested HMM is of improved training abilities. After applying the proposed HMM to the ridge orientation field, the matching cells are produced. These cells consist of transition, observation and initial probability matrices which will be used in the matching procedure. The proposed matching method has been evaluated using some creditable fingerprint databases such as FVC2000 DB2_A, FVC2004 DB3_A and DB4_A. The evaluation results confirm higher efficiency, robustness and accuracy for the proposed method compared with the previously proposed matching ones.

Nowadays, automatic computer-Aided Diagnosis (CAD) systems for grading different types of cancers like breast cancer are very prevalent. These systems employ histopathology slide images acquired by advanced and well-defined digital scanners. The previously proposed automatic or computer-aided systems for breast cancer grading, especially by counting mitoses, suffer from various types of deficiencies. The most important one is their low efficiency along with high complexity due to the huge amount of features. In this paper, two types of features with more flexibility and less complexity are employed. These features are Completed Local Binary Pattern (CLBP) as textural features and Stiffness Matrix as geometric, morphometric and shape-based features. In the proposed automatic mitosis detection system, these two features are fused with each other. The evaluation results are for histology Dataset H (Hamamatsu Nanozoomer Scanners) provided by Mitos-ICPR2012 contest sponsors. Employing a nonlinear RBF kernel support vector machine (SVM) classifier with parameter sigma which equals to 100, leads to an efficiency of 82%. The results are in the form of F-measure criterion which is a reliable and mostly common evaluation criterion for such biological systems.

Study of histopathological cancerous tissue is one of the most reliable ways to grade various types of cancers. The result of grading helps the physicians to diagnose and prescribe suitable prognosis. The focus of this paper is on a CAD for automatic analysis of breast cancer histopathological Images to count mitosis as an important criteria for the breast cancer grading. To achieve this aim, sets of specific digital histopathological data are used which are captured by particular microscopic scanners named as Aperio XT and Hamamatsu NanoZoomer scanners. In the proposed method, these acquired images are employed and processed based on digital image processing approaches like 2-D anisotropic diffusion as a pre-process and morphological process. For extraction of pixel-wise features from predetermined mitotic regions, an statistical approach based on color information such as maximum likelihood estimation is employed. To prevent misclassification of mitosis and non-mitosis objects, an object-wise completed local binary pattern (CLBP) is proposed to extract texture features robust against rotation and color-level changes, and finally support vector machine (SVM) is used to classify the extracted feature vectors. Having computed the evaluation criteria, our proposed method performs better f-measure (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) among the methods proposed by other participants at ICPR2012 Mitosis detection in breast cancer histopathological images.

Estimation of age and gender is one of the foremost challenges in computer vision and has lots of applications in recommender systems establishment, security and systems which deal with computer vision. In this paper, a method for age and gender estimation using facial images are proposed by focusing on the extraction of robust features existing in facial photos. The main stage for age estimation is done in two main steps. At the first step classification and extraction of the global features is done, and in the second step ratios which help distinguishing child (1 to 12 year-old children) from youth (13 to 40 year-old men) are described and in the next step by using the same procedure, seniors (from 41 to 80 years old) are separated from the two former groups. For gender estimation purpose, ratios computed in the previous step, are employed and finally the correct gender estimation is done.

A semi-fragile lossless data hiding (LDH) Scheme based on histogram distribution shift in integer wavelet transform (IWT) domain is proposed in this paper. In the proposed scheme, the transform approximation image is divided into non-overlapping blocks. In each block, the differences between the neighboring elements are computed and a histogram is made on the difference values. The secret data are embedded into the blocks based on a multi-level shifting mechanism of the histogram. The proposed method enables the exact recovery of the original host signal upon extracting the embedded information, if the watermarked image is not affected by any other process. The performance of proposed scheme is evaluated with respect to imperceptibility, robustness, and data payload capacity by simulations. Comparing with the state-of-the-art known techniques, the proposed method performs a higher performance.

Matching is an important step in any fingerprint recognition system. In this paper, a fingerprint matching technique based on hidden markov model is proposed. This method uses only the ridge orientation information around the reference point of registered fingerprint image. At the first step, fingerprint images are aligned according to a suitable reference point. Then in the second step, the ridge orientation field around this point is applied to the HMM based on an innovative topology. This topology provides many significant advantages such as simplicity, flexibility and generality. The selected orientation field forms specific feature vectors so that can be used for proposed HMM matching process. In the HMM matching process, the maximum likelihood between training and test feature vectors are tested according to a predefined threshold. For evaluating the proposed matching method, an artificial continuous classification had been applied over FVC2000 DB2_A. The results of experiment have proved the higher efficiency and robustness of the proposed method in comparison with the competing one.

As fingerprint verification depends strongly on the quality of fingerprint ridge orientation estimation, so the more accurate fingerprint ridge orientations estimate, the better verification will result in. In this paper, we have proposed a new technique for improving the coarse fingerprint orientation estimation smoothing using fingerprint orientation model based on 2D Fourier expansion with a special Modification on it (M-FOMFE). The modification we have used in this paper is taking into account the information of Coherence Matrix for fingerprint ridge orientation estimation. This matrix is used to retrieve the uncertainty of each ridge orientation block and improves the accuracy of old FOMFE coarse ridge orientation smoothing method. For evaluating the proposed method we use a fingerprint continuous classification system and also an exclusive one based on Poincare-Index algorithm for singular point detection. Compared to competing method, the experimental results show that the proposed method has better orientation estimation and classification results.