Imran Fareed Nizami

A New Gait Representation for Human Identification: Mass Vector

Gait is a new biometric aimed to recognize individuals by the way they walk. Gait recognition has recently an increasing interest from researchers due to several advantages. In this paper, we have proposed a new representation for human gait recognition which is called as mass vector. The mass vector along a given row is defined as the number of pixels with a nonzero value in a given row of the binarized silhouette of a walking person. Sequences of temporally ordered mass vector are used to represent a gait of an individual. We use the dynamic time-warping (DTW) approach for matching so that non-linear time normalization may be used to deal with the naturally-occurring changes in walking speed. Experimental results show that mass vector has a high discriminative power for gait recognition. The recognition rate is around 96.25% in a canonical viewing angle in NLPR gait database by using mass vector. Our proposed system outperforms previous works.

Multi-view gait recognition fusion methodology

This paper presents a multi-view gait recognition algorithm for identification at a distance. We make use of two well known and effective gait representations namely Motion Silhouette Image (MSI) and gait energy image (GEI). MSI and GEI inherently capture the spatiotemporal characteristics of gait. We show that the individual recognition performance of MSI and GEI can be improved by using a fusion methodology. The features for MSI and GEI images are extracted using Independent Component Analysis (ICA) which is used widely in such applications. Extreme Learning Machine (ELM) classifier is then used for classification. ELM is a multiclass classifier which offers the advantage of less time consumption and high performance. The results are fused at score level making use of fusion rules such as min and max [17] to make the algorithm robust, reliable and to improve the performance of the system. Our approach is tested on the NLPR gait database. The NLPR gait database corresponds to 20 subjects, each subject has 4 sequences and there are 3 viewing angles (0deg, 45deg and 90deg) for each person. The results on the dataset show that the fusion gives good performance for the 3 views considered in this paper.

Human identification based on gait analysis

In this paper, we have proposed a new representation for human gait recognition which is called as mass vector. The mass vector along a given row is defined as the number of pixels with a nonzero value in a given row of the binarized silhouette of a walking person. Sequences of temporally ordered mass vector are used to represent a gait of an individual. Besides, different gait features are extracted from the mass vector such as the down-sampled mass vectors and the principal components of mass vectors. We use the dynamic time-warping (DTW) approach for matching so that non-linear time normalization may be used to deal with the naturally-occurring changes in walking speed. Experimental results show that mass vector has a higher discriminative power than previous works for gait recognition.

An efficient design of a nearest neighbor classifier for various-scale problems

By appropriate editing of the reference set and judicious selection of features, we can obtain an optimal nearest neighbor (NN) classifier that maximizes the accuracy of classification and saves computational time and memory resources. In this paper, we propose a new method for simultaneous reference set editing and feature selection for a nearest neighbor classifier. The proposed method is based on the genetic algorithm and employs different genetic encoding strategies according to the size of the problem, such that it can be applied to classification problems of various scales. Compared with the conventional methods, the classifier uses some of the considered references and features, not all of them, but demonstrates better classification performance. To demonstrate the performance of the proposed method, we perform experiments on various databases.

Automatic gait recognition based on probabilistic approach

A simple probabilistic method for online video based human identification is introduced in this article. The proposed method is based on a modified version of Motion Silhouette images (MSI) and recursive probability accumulation. The modified version of MSI is named the Moving Motion Silhouette Image (MMSI). Identification probability is accumulated recursively in a Bayesian framework to draw a single conclusion from the whole gait sequence. The probability is named the accumulated posterior probability (APP) and denotes the probability based on all the information available up to now. The proposed method is tested on the well‐known publicly available NLPR and SOTON gait databases. The experimental results demonstrate the effectiveness of the proposed algorithm and indicate the fact that using MMSI and APP for information fusion yields higher recognition rates as compared to previous gait recognition systems.

Efficient feature selection for Blind Image Quality Assessment based on natural scene statistics

Blind Image Quality Assessment (BIQA) has received considerable importance with the increase in the use of multimedia in our daily lives. The main objective of BIQA is to predict the quality of distorted images without any prior information about the original image. In this work, we propose an efficient feature selection method for blind image quality assessment based on natural scene statistics i.e., Distortion Identification-based Image Verity and Integrity Evaluation (DIIVINE). The proposed method produces better results for non-reference image quality assessment by selecting features, which produce the best Spearman Rank Order Correlation Constant (SROCC) scores averaged over 1000 random runs. The experimental results conducted on the LIVE database show that the proposed method strongly correlates to the subjective mean observer score and is competitive to the state-of-the-art image quality assessment techniques with a minimum number of features that reduces the computational expense.

Fusion algorithm for Integrated Face and Gait Identification

Identification of humans from multiple view points is an important task for surveillance and security purposes. For optimal performance the system should use the maximum information available from sensors. Multimodal biometric systems are capable of utilizing more than one physiological or behavioral characteristic for enrollment, verification, or identification. Since gait alone is not yet established as a very distinctive feature, this paper presents an approach to fuse face and gait for identification. In this paper we will use the single camera case i.e. both the face and gait recognition is done using the same set of images captured by a single camera. The aim of this paper is to improve the performance of the system by utilizing the maximum amount of information available in the images. Fusion is considered at decision level. The proposed algorithm is tested on the NLPR database.

New feature selection algorithms for no-reference image quality assessment

No reference image quality assessment (NR-IQA) is a challenging task since reference images are usually unavailable in real world scenarios. The performance of NR-IQA techniques is vastly dependent on the features utilized to predict the image quality. Many NR-IQA techniques have been proposed that extract features in different domains like spatial, discrete cosine transform and wavelet transform. These NR-IQA techniques have the possibility to contain redundant features, which result in degradation of quality score prediction. Recently impact of general purpose feature selection algorithms on NR-IQA techniques has shown promising results. But these feature selection algorithms have the tendency to select irrelevant features and discard relevant features. This paper presents fifteen new feature selection algorithms specifically designed for NR-IQA, which are based on Spearman rank ordered correlation constant (SROCC), linear correlation constant (LCC), Kendall correlation constant (KCC) and root mean squared error (RMSE). The proposed feature selection algorithms are applied on the extracted features of existing NR-IQA techniques. Support vector regression (SVR) is then applied to selected features to predict the image quality score. The fifteen newly proposed feature selection algorithms are evaluated using eight different NR-IQA techniques over three commonly used image quality assessment databases. Experimental results show that the proposed feature selection algorithms not only reduce the number of features but also improve the performance of NR-IQA techniques. Moreover, features selection algorithms based on SROCC and its combination with LCC, KCC and RMSE perform better in comparison to other proposed algorithms.

A wavelet frames + K-means based automatic method for lung area segmentation in multiple slices of CT scan

Computer assisted detection of lung nodules offers a more accurate method of nodule detection which leads to reliable diagnosis of lung cancer. Lung segmentation is a first step in the process of automatic detection of nodules. In this paper, we propose a wavelet packet frames based approach for effective lung segmentation. The proposed algorithm selects the optimal wavelet representation that is a collection of wavelet packet frames. The frames are subsequently used for clustering of coefficients using k-means clustering, which leads to the segmented lung region. The algorithm is tested on the one publicly available dataset containing 350 images and CT scan dataset of 5 local patients containing a total of 71 images. Accurate segmentation of lung is acquired with average difference in pixels from the ground truth being as low as 1.34±0.451. Furthermore, the proposed technique is fully automated and is capable of segmenting lung in multiple slices with no manual intervention or change in parameters.