Naseer Al-Jawad

Gait recognition based on Kinect sensor

This paper presents gait recognition based on human skeleton and trajectory of joint points captured by Microsoft Kinect sensor. In this paper Two sets of dynamic features are extracted during one gait cycle: the first is Horizontal Distance Features (HDF) that is based on the distances between (Ankles, knees, hands, shoulders), the second set is the Vertical Distance Features (VDF) that provide significant information of human gait extracted from the height to the ground of (hand, shoulder, and ankles) during one gait cycle. Extracting these two sets of feature are difficult and not accurate based on using traditional camera, therefore the Kinect sensor is used in this paper to determine the precise measurements. The two sets of feature are separately tested and then fused to create one feature vector. A database has been created in house to perform our experiments. This database consists of sixteen males and four females. For each individual, 10 videos have been recorded, each record includes in average two gait cycles. The Kinect sensor is used here to extract all the skeleton points, and these points are used to build up the feature vectors mentioned above. K-nearest neighbor is used as the classification method based on Cityblock distance function. Based on the experimental result the proposed method provides 56% as a recognition rate using HDF, while VDF provided 83.5% recognition accuracy. When fusing both of the HDF and VDF as one feature vector, the recognition rate increased to 92%, the experimental result shows that our method provides significant result compared to the existence methods.

Arabic handwritten: pre-processing and segmentation

This paper is concerned with pre-processing and segmentation tasks that influence the performance of Optical Character Recognition (OCR) systems and handwritten/printed text recognition. In Arabic, these tasks are adversely effected by the fact that many words are made up of sub-words, with many sub-words there associated one or more diacritics that are not connected to the sub-words body; there could be multiple instances of sub-words overlap. To overcome these problems we investigate and develop segmentation techniques that first segment a document into sub-words, link the diacritics with their sub-words, and removes possible overlapping between words and sub-words. We shall also investigate two approaches for pre-processing tasks to estimate sub-words baseline, and to determine parameters that yield appropriate slope correction, slant removal. We shall investigate the use of linear regression on sub-words pixels to determine their central x and y coordinates, as well as their high density part. We also develop a new incremental rotation procedure to be performed on sub-words that determines the best rotation angle needed to realign baselines. We shall demonstrate the benefits of these proposals by conducting extensive experiments on publicly available databases and in-house created databases. These algorithms help improve character segmentation accuracy by transforming handwritten Arabic text into a form that could benefit from analysis of printed text.

Breast border extraction and pectoral muscle removal in MLO mammogram images

In this paper, we propose a new method for breast border extraction, artifact removal and removal of annotations typically found in the background of mammograms. The proposed method uses adaptive local thresholding to create an initial binary mask for an image. This is followed by the use of morphological operations to remove background artifacts. Then an adaptive algorithm is proposed to automatically detect and remove the pectoral muscle depending on the gray-level intensity values. Preliminary results of experiments conducted on the Mini-MIAS database (Mammographic Image Analysis Society, London, U.K.) show that the proposed method achieves a near 100% success rate for breast contour extraction and the proposed method for pectoral muscle removal achieves nearly 89% accuracy. More importantly, the proposed pre-processing techniques improved the mammogram classification results when compared to using previous pre-processing methods.

Wavelet based image quality self measurements

Noise in general is considered to be degradation in image quality. Moreover image quality is measured based on the appearance of the image edges and their clarity. Most of the applications performance is affected by image quality and level of different types of degradation. In general measuring image quality and identifying the type of noise or degradation is considered to be a key factor in raising the applications performance, this task can be very challenging. Wavelet transform now a days, is widely used in different applications. These applications are mostly benefiting from the wavelet localisation in the frequency domain. The coefficients of the high frequency sub-bands in wavelet domain are represented by Laplace histogram. In this paper we are proposing to use the Laplace distribution histogram to measure the image quality and also to identify the type of degradation affecting the given image. Image quality and the level of degradation are mostly measured using a reference image with reasonable quality. The discussed Laplace distribution histogram provides a self testing measurement for the quality of the image. This measurement is based on constructing the theoretical Laplace distribution histogram of the high frequency wavelet sub-band. This construction is based on the actual standard deviation, then to be compared with the actual Laplace distribution histogram. The comparison is performed using histogram intersection method. All the experiments are performed using the extended Yale database.

Effects of User Age on Smartphone and Tablet Use, Measured with an Eye-Tracker via Fixation Duration, Scan-Path Duration, and Saccades Proportion

The design of user interfaces plays an important role in human computer interaction, especially for smartphones and tablet devices. It is very important to consider the interface design of smartphones for elderly people in order for them to benefit from the variety applications on such devices. The aim of this study is to investigate the effects of user age as well as screen size on smartphone/tablet use. We evaluated the usability of smartphone interfaces for three different age groups: elderly age group (60+ years), middle age group (40-59 years) and younger age group (20-39 years). The evaluation is performed using three different screen sizes of smartphone and tablet devices: 3.2”, 7”, and 10.1” respectively. An eye-tracker device was employed to obtain three metrics: fixation duration, scan-path duration, and saccades amplitude. Two hypothesis were considered. First, elderly users will have both local and global processing diffieculties on smartphone/tablet use than other age groups. Second, all user age groups will be influnced by screen sizes; small screen size will have smaller saccades proportion indicating uneasy interface broswing compared to large screen size. All these results have been statistically evaluated using 2-way ANOVA.

Simultaneous video compression and encryption for real-time secure transmission

Video compression and encryption became very essential in secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important. In this paper we propose a new technique for video compression and encryption. The compression bases on hybridizing DWT, DCT and vector quantization. The compression algorithm includes two major steps, reference frame encoding and current frame encoding based on the reference frame. The encryption algorithm utilizes two LFSRs seeded with three secret keys to scramble the significant wavelet coefficients multiple times. Both algorithms are applied simultaneously in the wavelet domain. Experimental results show that the proposed algorithms have the following properties; high compression, acceptable quality, and resistance to the statistical and frequency attack with low computational overhead.

Gait recognition using spatio-temporal silhouette-based features

This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.

Effect of using different cover image quality to obtain robust selective embedding in steganography

One of the common types of steganography is to conceal an image as a secret message in another image which normally called a cover image; the resulting image is called a stego image. The aim of this paper is to investigate the effect of using different cover image quality, and also analyse the use of different bit-plane in term of robustness against well-known active attacks such as gamma, statistical filters, and linear spatial filters. The secret messages are embedded in higher bit-plane, i.e. in other than Least Significant Bit (LSB), in order to resist active attacks. The embedding process is performed in three major steps: First, the embedding algorithm is selectively identifying useful areas (blocks) for embedding based on its lighting condition. Second, is to nominate the most useful blocks for embedding based on their entropy and average. Third, is to select the right bit-plane for embedding. This kind of block selection made the embedding process scatters the secret message(s) randomly around the cover image. Different tests have been performed for selecting a proper block size and this is related to the nature of the used cover image. Our proposed method suggests a suitable embedding bit-plane as well as the right blocks for the embedding. Experimental results demonstrate that different image quality used for the cover images will have an effect when the stego image is attacked by different active attacks. Although the secret messages are embedded in higher bit-plane, but they cannot be recognised visually within the stegos image.

Human gait gender classification based on fusing spatio-temporal and wavelet statistical features

Gait recognition is one of the biometric recognition systems that do not require observed subjects attention and assistance. This paper proposes gender classification based on human gait. Gender is an important demographic attribute of people that can play a significant role in automatic gait recognition, the perception of gender determines social interactions. Humans are very accurate at recognizing gender from face, voice or the manner in which an individual walks. In our proposed technique we focus on using three different types of features; Spatio-Temporal Model, Leg Motion Detection, and Statistical Wavelet Model. These features have different characteristics to be used in gender recognition system based on gait recognition. For testing the performance of our method we used CASIA B gait database this paper proposes a way of testing the performance by selecting randomly equal subset of males and females then run the experiment repeatedly many times to cover the entire subjects in the database. This testing approach makes the achieved result more reliable compared with the existing approaches. Two different classification methods used in our proposal; K-Nearest Neighbors and Support Vector Machine. Our experimental results, of 96.47% classification rate in average, show that our approach is providing more trustworthy accuracy compared with the existent approaches.

Sub-word based Arabic handwriting analysis for writer identification

Analysing a text or part of it is key to handwriting identification. Generally, handwriting is learnt over time and people develop habits in the style of writing. These habits are embedded in special parts of handwritten text. In Arabic each word consists of one or more sub-word(s). The end of each sub-word is considered to be a connect stroke. The main hypothesis in this paper is that sub-words are essential reflection of Arabic writers habits that could be exploited for writer identification. Testing this hypothesis will be based on experiments that evaluate writers identification, mainly using K nearest neighbor from group of sub-words extracted from longer text. The experimental results show that using a group of sub-words could be used to identify the writer with a successful rate between 52.94 % to 82.35% when top1 is used, and it can go up to 100% when top5 is used based on K nearest neighbor. The results show that majority of writers are identified using 7 sub-words with a reliability confident of about 90% (i.e. 90% of the rejected templates have significantly larger distances to the tested example than the distance from the correctly identified template). However previous work, using a complete word, shows successful rate of at most 90% in top 10.