Almabrok Essa

Local directional pattern of phase congruency features for illumination invariant face recognition

An illumination-robust face recognition system using Local Directional Pattern (LDP) descriptors in Phase Congruency (PC) space is proposed in this paper. The proposed Directional Pattern of Phase Congruency (DPPC) is an oriented and multi-scale local descriptor that is able to encode various patterns of face images under different lighting conditions. It is constructed by applying LDP on the oriented PC images. A LDP feature is obtained by computing the edge response values in eight directions at each pixel position and encoding them into an eight bit binary code using the relative strength magnitude of these edge responses. Phase congruency and local directional pattern have been independently used in the field of face and facial expression recognition, since they are robust to illumination changes. When the PC extracts the discontinuities in the image such as edges and corners, the LDP computes the edge response values in different directions and uses these to encode the image texture. The local directional pattern descriptor on the phase congruency image is subjected to principal component analysis (PCA) for dimensionality reduction for fast and effective face recognition application. The performance evaluation of the proposed DPPC algorithm is conducted on several publicly available databases and observed promising recognition rates. Better classification accuracy shows the superiority of the LDP descriptor against other appearance-based feature descriptors such as Local Binary Pattern (LBP). In other words, our result shows that by using the LDP descriptor the Euclidean distance between reference image and testing images in the same class is much less than that between reference image and testing images from the other classes.

Intrusion detection in aerial imagery for protecting pipeline infrastructure

We present an automated mechanism that can detect and issue warnings of machinery threat such as the presence of construction vehicles on pipeline right-of-way. The proposed scheme models the human visual perception concepts to extract fine details of objects by utilizing the corners and gradient histogram information in pyramid levels. Two real-world aerial image datasets are used for testing and evaluation.

A modular approach for key-frame selection in wide area surveillance video analysis

This paper presents an efficient preprocessing algorithm for big data analysis. Our proposed key-frame selection method utilizes the statistical differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames. We anticipate that such key frame selection technique will have significant impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery. Three real-world datasets are used for evaluation and testing and the observed results are encouraging.

Volumetric Directional Pattern for Spatial Feature Extraction in Hyperspectral Imagery

In this letter, we propose to use an enhanced version of volumetric directional pattern to efficiently extract rich spatial context information in the hyperspectral imagery (HSI). The proposed technique fuses the texture information from three consecutive bands in the input HSI. The extracted local image texture features for each pixel of interest are then fed into an extreme learning machine classifier to assign object category. The experimental results on three standard hyperspectral data sets demonstrate the effectiveness of the proposed method for HSI classification compared with that of a set of state-of-the-art spatial extraction methods.

Multifeature fusion for automatic building change detection in wide-area imagery

Abstract. A strategy for detecting changes in known building regions in multitemporal visible and near-infrared imagery based on a linear combination of independent features is presented. Features identified for building and background detection include vegetation, texture, shadow intensity, and distance from known road areas. The resulting building candidates are classified by shape using a unique difference of Gaussian technique. Building regions reported in the reference dataset that indicate the initial observation time are revisited to check for changes in building candidates not identified in the feature fusion strategy. The performance of the proposed technique is tested on real-world aerial imagery and is evaluated visually and quantitatively. Compared with the gradient and normalized difference vegetation index-based building detection methods, the proposed fusion methodology yields better results. For building detection, it provided a completeness result of an average 82.08% and building change detection completeness result of an average 85.67% in our evaluations with five sample images, which included rural, suburban, and urban areas.

Face recognition based on modular histogram of oriented directional features

This paper presents an illumination invariant face recognition system that uses local directional pattern descriptor and modular histogram. The proposed Modular Histogram of Oriented Directional Features (MHODF) is an oriented local descriptor that is able to encode various patterns of face images under different lighting conditions. It employs the edge response values in different directions to encode each sub-image texture and produces multi-region histograms for each image. The edge responses are very important and play the main role for improving the face recognition accuracy. Therefore, we present the effectiveness of using different directional masks for detecting the edge responses on face recognition accuracy, such as Prewitt kernels, Kirsch masks, Sobel kernels, and Gaussian derivative masks. The performance evaluation of the proposed MHODF algorithm is conducted on several publicly available databases and observed promising recognition rates.

Automatic building change detection in wide area surveillance

We present an automated mechanism that can detect and characterize the building changes by analyzing airborne or satellite imagery. The proposed framework can be categorized into three stages: building detection, boundary extraction and change identification. To detect the buildings, we utilize local phase and local amplitude from monogenic signal to extract building features for addressing issues of varying illumination. Then a support vector machine with Radial basis kernel is used for classification. In the boundary extraction stage, a level-set function with self-organizing map based segmentation method is used to find the building boundary and compute physical area of the building segments. In the last stage, the change of the detected building is identified by computing the area differences of the same building that captured at different times. The experiments are conducted on a set of real-life aerial imagery to show the effectiveness of the proposed method.

Video-to-video Pose and Expression Invariant Face Recognition using Volumetric Directional Pattern

Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are used to match with all the videos in the database. To make the approach computationally simple and easy to extend, key-frame extraction method is employed. Therefore, only the frames which contain important information of the video can be used for further processing instead of analysing all the frames in the video. The performance evaluation of the proposed VDP algorithm is conducted on a publicly available database (YouTube celebrities’ dataset) and observed promising recognition rates.

Multi-feature fusion based approach for robust face recognition

How to describe an image accurately with the most useful information is the key issue of any face recognition task. Therefore, finding efficient and discriminative facial information that should be stable under different conditions of the image acquisition process is a huge challenge. Most existing approaches use only one type of features. In this paper, we argue that a robust face recognition technique requires several different kinds of information to be taken into account, suggesting the incorporation of several feature sets into a single fused one. Therefore, a new technique that combines the facial shape with the local structure and texture of the face image is proposed, namely multi-feature fusion (MFF). It is based on local boosted features (LBF) and Gabor wavelets techniques. Given an input image, the LBF histogram and Gabor features histogram are built separately. Then a final MFF feature descriptor is formed by concatenating these three histograms, which feeds to the support vector machine (SVM) classifier to recognize the face image. The proposed MFF approach is evaluated on three different face datasets and provided promising results.

Integrated color, shape and texture features for object classification on oil/gas pipeline right-of-ways

Pipeline right-of-way (ROW) monitoring and safety pre-warning is an important way to guarantee a safe operation of oil/gas transportation. Any construction equipment or heavy vehicle intrusion is a potential safety hazard to the pipeline infrastructure. Therefore, we propose a novel technique that can detect and classify an intrusion on oil/gas pipeline ROW. The detection part has been done based on our previous work, where we built a robust feature set using a pyramid histogram of oriented gradients in the Fourier domain with corresponding weights. Then a support vector machine (SVM) with radial basis kernel is used to distinguish threat objects from background. For the classification part, the object can be represented by an integrated color, shape and texture (ICST) feature set, which is a combination of three different feature extraction techniques viz. the color histogram of HSV (hue, saturation, value), histogram of oriented gradient (HOG), and local binary pattern (LBP). Then two decision making models based on K-nearest neighbor (KNN) and SVM classifier are utilized for automatic object identification. Using real-world dataset, it is observed that the proposed method provides promising results in identifying the objects that are present on the oil/gas pipeline ROW.