A Study on Facial Expression Recognition Using Local Binary Pattern

How to get the proper combination of feature extraction and classification is still crucial in facial expression recognition, and it has been addressed conducted over two decades. Hence, if inadequate features are used, even the best classifier could fail to achieve the accurate recognition. Therefore, Local Binary Pattern (LBP) is used as a feature extraction technique for facial expressions recognition where it is evaluated based on statistical local features. LBP is proven successful technique by the recent study due to its speed and discrimination performance aside of robust to low-resolution images. For the classification, Support Vector Machine is chosen, and the algorithm is implemented in MATLAB and tested on JAFFE (Japanese Female Facial Expressions) database in order to achieve the objectives and the goal of this research which is to obtain high accuracy in facial expressions and identify the seven basic facial expressions. The performance of feature extraction and classification is evaluated based on the recognition accuracy. The observation on results obtained in facial expressions recognition rate indicated the effectiveness of the proposed algorithm based on SVM-LBP features

Image Enhancement Technique at Different Distance for Iris Recognition

Capturing eye images within visible wavelength illumination in non-cooperative environment lead to the low quality of eye images. Thus, this study is motivated to investigate the effectiveness of image enhancement technique that able to solve the abovementioned issue. A comparative study has been conducted in which three image enhancement techniques namely Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were evaluated and analysed. UBIRIS.v2 eye image database was used as a dataset to evaluate those techniques. Moreover, each of enhancement techniques were tested against different distance of eye image captured. Results were compared in term of image interpretation by using Peak-Signal Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean Absolute Error (MAE). The effectiveness of the enhancement techniques on different distance of image captured was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR). As a result, CLAHE has proven to be the most reliable technique in enhancing the eye image which improved the localization accuracy by 7%. In addition, the results showed that by implementing CLAHE technique at four meter distance was an ideal distance to capture eye images in non-cooperative environment where it provides high recognition accuracy, 74%

Comparative Study of Different Window Sizes Setting in Median Filter for Off-angle Iris Recognition

Iris recognition is one of the most popular biometric recognition that has increased in the number of acceptance user gradually because of the reliability and accuracy provided by this system. However, this accuracy is highly correlated with the quality of iris image captured. Thus, a poor quality of the image captured required an enhancement technique. This study aims to identify the optimum window size for the median filter. Identifying the optimum window size setting required template matching value result of the off-angle iris recognition. The lowest value obtained showed that the window size applied was optimized. The result of this study demonstrated, for WVU-OA dataset for 15 degrees off-angle iris of right and left eyes, the window size of [5 5] and [7 7] respectively are optimum to maximize the median filter function. Meanwhile, for 30 degrees off-angle iris of right and left eyes data, the optimum windows size proposed are [7 7] and [5 5] respectively. On the other hand, analysis using UBIRIS dataset showed that the optimum window size for 30 degrees off-angle iris, both right and left eye is [7 7] which is able to maximize the performance of the median filter. In conclusion, the effective value to be applied to all dataset are [5 5] and [7 7] because in most cases it provides a better template matching compared to without applying the filtering method

Extended TvX: A New Method Feature Based Semantic Similarity for Multiple Ontology

Semantic similarity between the terms is the main phase in information retrieval and information integration, which requires semantic content matching. Semantic similarity function is important in psychology, artificial intelligence and cognitive science. The problem of integrating various sources is the matching between ontological concepts. In this paper, we proposed to develop this method by analyzing the semantic similarity between the modeled taxonomical knowledge and features in different ontology. This paper contains a review on semantic similarity and multiple ontology that focuses on the feature-based approach. Besides that, we proposed a method, namely a semantic similarity that overcomes the limitation of different features of terms compared. As a result, we are able to develop a better method that improves the accuracy of the similarity measurement

A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Biometric recognition is gaining attention as most of the organization is seeking for a more secure verification method for user access and other security application. There are a lot of biometric systems that exist which are iris, hand geometry and fingerprint recognition. In biometric system, iris recognition is marked as one of the most reliable and accurate biometric in term of identification. However, the performance of iris recognition is still doubted whether the iris recognition can generate higher accuracy when involving twin iris data. So, specific research by using twin data only needs to be done to measure the performance of recognition. Besides that, a comparative study is carried out using two template matching technique which are Hamming Distance and Euclidean Distance to measure the dissimilarity between the two iris template. From the comparison of the technique, better template matching technique also can be determined. The experimental result showed that iris recognition can distinguish twin as it can distinguish two different, unrelated people as the result obtained showed the good separation between intra and interclass and both techniques managed to obtain high accuracy. From the comparison of template matching technique, Hamming Distance is chosen as better technique with low False Rejection Rate, low False Acceptance Rate and high Total Success Rate with the value of 2.5%, 8.75% and 96.48% respectively

Image enhancement technique at different distance for Iris recognition

Capturing eye images within visible wavelength illumination in the non-cooperative environment lead to the low quality of eye images. Thus, this study is motivated to investigate the effectiveness of image enhancement technique that able to solve the abovementioned issue. A comparative study has been conducted in which three image enhancement techniques namely Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were evaluated and analysed. UBIRIS.v2 eye image database was used as a dataset to evaluate those techniques. Moreover, each of enhancement techniques was tested against the different distance of eye image captured. Results were compared in terms of image interpretation by using Peak-Signal Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean Absolute Error (MAE). The effectiveness of the enhancement techniques on the different distance of image captured was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR). As a result, CLAHE has proven to be the most reliable technique in enhancing the eye image which improved the localization accuracy by 7%. In addition, the results showed that by implementing CLAHE technique at a four-meter distance was an ideal distance to capture eye images in a non-cooperative environment where it provides high recognition accuracy, 74%

Comparative study of different window sizes setting in median filter for off-angle iris recognition

Iris recognition is one of the most popular biometric recognition that has increased in the number of acceptance user gradually because of the reliability and accuracy provided by this system. However, this accuracy is highly correlated with the quality of iris image captured. Thus, a poor quality of the image captured required an enhancement technique. This study aims to identify the optimum window size for the median filter. Identifying the optimum window size setting required template matching value result of the off-angle iris recognition. The lowest value obtained showed that the window size applied was optimized. The result of this study demonstrated, for WVU-OA dataset for 15 degrees off-angle iris of right and left eyes, the window size of [5 5] and [7 7] respectively are optimum to maximize the median filter function. Meanwhile, for 30 degrees off-angle iris of right and left eyes data, the optimum windows size proposed are [7 7] and [5 5] respectively. On the other hand, analysis using UBIRIS dataset showed that the optimum window size for 30 degrees off-angle iris, both right and left eye is [7 7] which is able to maximize the performance of the median filter. In conclusion, the effective value to be applied to all dataset are [5 5] and [7 7] because in most cases it provides a better template matching compared to without applying the filtering method

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks