Representation of facial expression categories in continuous arousal–valence space: Feature and correlation

Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and the investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal–valence (AV) dimensional space. The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories.     

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult.By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system.We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.     

Detecting plagiarized mobile apps using API birthmarks

This paper addresses the problem of detecting plagiarized mobile apps. Plagiarism is the practice of building mobile apps by reusing code from other apps without the consent of the corresponding app developers. Recent studies on third-party app markets have suggested that plagiarized apps are an important vehicle for malware delivery on mobile phones. Malware authors repackage official versions of apps with malicious functionality, and distribute them for free via these third-party app markets. An effective technique to detect app plagiarism can therefore help identify malicious apps. Code plagiarism has long been a problem and a number of code similarity detectors have been developed over the years to detect plagiarism. In this paper we show that obfuscation techniques can be used to easily defeat similarity detectors that rely solely on statically scanning the code of an app. We propose a dynamic technique to detect plagiarized apps that works by observing the interaction of an app with the underlying mobile platform via its API invocations. We propose API birthmarks to characterize unique app behaviors, and develop a robust plagiarism detection tool using API birthmarks.     

A low-power,micromachined, double spiral hotplate for MEMS gas sensors

This paper presents the design, fabrication and reliability testing of a double spiral platinum-based MEMS hotplate for gas sensing applications. The structure of MEMS hotplate consists of a 0.7 µm-thick thermally grown SiO₂ membrane of size 120 µm × 120 µm over which a double spiral platinum resistor is laid out. The hotplate membrane is supported by its four arms connected to the Si-substrate. The design and simulation of the hotplate structure was carried out using MEMS-CAD Tool COVENTORWARE. Based on the design, a double spiral platinum resistor of 103 Ω is fabricated on SiO₂ membrane using lift-off technique. The platinum deposition is carried out using DC sputtering technique. Bulk micromachining of Si is done from front side of the structure to create the suspended SiO₂ membrane. The temperature coefficient of resistance (TCR) of platinum is measured and found to be 2.19 × 10^?3/ °C. The TCR value is used for temperature estimation of the hotplate. The test results show that the microhotplate consumes only 20 mW power when heated up to 500 °C. For reliability testing of fabricated structure, the hotplate is continuously operated at 300 °C for 1.8 h. Also, it can sustain at least 61 cycles pulse-mode operation at 530 °C with ultra-low resistance and temperature drifts. The structure can sustain a maximum temperature and current of 611 °C and 11.55 mA respectively without any damage.     

A Computationally Efficient and Scalable Key Management Scheme for Access Control of Media Delivery in Digital Pay-TV Systems

Multimedia Tools and Applications - In today’s Internet era, group communications in multimedia applications are becoming more and more popular. The issues of controlling illegal access to...     

Combining a new data classification technique and regression analysis to predict the Cost-To-Serve new customers

Identifying the Cost-To-Serve (CTS) of customers is one of the most challenging problems in Supply Chain Management because of the diversity in their business activities. For the particular case of the industrial gas business, we are interested in predicting the cost to deliver bulk (liquefied) gas to new customers using a multifactor linear regression model. Developing a single model, i.e. analyzing the observations all at once, produces poor prediction results. Therefore prior to the regression analysis, a new supervised learning technique is used to group customers who are similar in some sense. Classes of customers are represented by hyper-boxes and a linear regression model is subsequently built within each class. The combination of data classification and regression is proven to increase the accuracy of the prediction.Two Mixed-Integer-Linear Programming (MILP) models are developed for data classification purposes. Although we are dealing with a supervised learning method, classes are not predefined in our case. Rather, we input a continuous “classification” attribute that is optimally discretized by the MILP’s in order to minimize the number of misclassifications. Therefore our data classification model offers a broader range of applications. A number of illustrative examples are used to prove the effectiveness of the proposed approach.     

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.      

An efficient new method for the detection of QRS in electrocardiogram

A simple and efficient new method for QRS detection in Electrocardiogram (ECG) is proposed in this paper. Initially data is preprocessed using two stage median filter for removing baseline drift. The second stage enhances the peaks of ECG wave components by using sixth power of signal. The next stage identifies the QRS complex by taking a variable window size. The detection sensitivity (Se) and positive predictivity (+P) of CSE (Common Standards for Quantitative Electrocardiography) measurement database, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) Arrhythmia database, European ST-T database and QT database are Se 99.51 & +P 99.69%, Se 99.21 & +P 99.34%, Se 99.53 & +P 99.72% and Se 99.87 & +P 99.95% respectively. These four standard databases used to perform QRS detection considered 368 cases, tested 1,006,168 beats and achieved overall average sensitivity 99.52% and positive predictivity 99.69%. The MIT/BIH Noise Stress Test Database also tested by proposed method.     

Image retrieval using efficient local-area matching

We present an efficient and accurate method for retrieving images based on color similarity with a given query image or histogram. The method matches the query against parts of the image using histogram intersection. Efficient searching for the best matching subimage is done by pruning the set of subimages using upper bound estimates. The method is fast, has high precision and recall and also allows queries based on the positions of one or more objects in the database image. Experimental results showing the efficiency of the proposed search method, and high precision and recall of retrieval are presented. Received: 20 January 1997 / Accepted: 5 January 1998