Tabassam Nawaz

Infotainment devices control by eye gaze and gesture recognition fusion

This paper presents a novel concept for controlling the consumer devices such as MP3 player or other daily life appliances by fusion of eye gaze and gesture recognition methodologies. Such system is deployable for virtually controlling the consumer devices anywhere in the home and office. The usability of the system is also tested with patients at the intensive care units of the hospitals where the patients may not able to operate the consumer devices in a regular way. The proposed system consists of a video processing based embedded system, a CCD camera and situated display. Physical control options are displayed over the situated display. Selection / de-selection process of displayed control option over situated display is accomplished by analyzing the video sequence captured by the CCD camera. Eye gaze estimation and head gesture recognition algorithms analyze the video sequence and reveal the control command that has to be sent to the infotainment or other consumer devices.

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.

A survey on block based copy move image forgery detection techniques

In todays modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

Iris detection based on pupil prospect point and horizontal projections

Iris is one of the most discriminating human physiological traits being used for personal human identification. The success of iris based system is highly relied on the accurately captured and precisely segmented iris image. False rejection rate has been a major challenge in the success of such system which primarily results from inaccurate iris segmentation. Most of the presented algorithms on iris segmentation considers pupil as perfect circle. However, according to observation this is not true in all the cases. In addition, a little angular shift in position of subject iris can further deteriorate the performance of algorithms based on circular assumption. To improve the quality of segmentation, an effective algorithm is proposed for iris segmentation which takes into consideration issues related to irregular pupil boundary as well as computation intensive nature of the prevailing algorithms. Contrary to all the previous approaches, the proposed algorithm is based on detection of pupil prospect point within the pupil region and utilizes bi-directional horizontal projections and distance parameters to detect the pupillary as well as limbus boundaries. The processing involved is linear in nature. Simulation of the proposed algorithm is done in Matlab.

Forensic analysis of copy-move forgery in digital images using the stationary wavelets

In this modern life, the digital images are being considered the main source of information sharing. The digital images are being used in various fields such as medical imaging, news reporting, crime investigation, insurance claims etc. However, in the presence of sophisticated image editing tools, the credibility of digital images is the main concern. The copy-move forgery (CMF) is a most popular forgery attack. In CMF, a region is duplicated elsewhere in the same image for producing a forged image. Thus, for detecting this particular artifact an algorithm is presented that utilizes Stationary Wavelet Transform (SWT). The experimental results demonstrate that the algorithm is capable of detecting duplicated blocks precisely and identify multiple CMF effectively, even when the images are contaminated by blurring and noise. The performance of our algorithm in comparison with other systems proves the efficacy of the system with a noticeable increase in detection ratios.

A hybrid technique for speech segregation and classification using a sophisticated deep neural network

Recent research on speech segregation and music fingerprinting has led to improvements in speech segregation and music identification algorithms. Speech and music segregation generally involves the identification of music followed by speech segregation. However, music segregation becomes a challenging task in the presence of noise. This paper proposes a novel method of speech segregation for unlabelled stationary noisy audio signals using the deep belief network (DBN) model. The proposed method successfully segregates a music signal from noisy audio streams. A recurrent neural network (RNN)-based hidden layer segregation model is applied to remove stationary noise. Dictionary-based fisher algorithms are employed for speech classification. The proposed method is tested on three datasets (TIMIT, MIR-1K, and MusicBrainz), and the results indicate the robustness of proposed method for speech segregation. The qualitative and quantitative analysis carried out on three datasets demonstrate the efficiency of the proposed method compared to the state-of-the-art speech segregation and classification-based methods.