Automatic face recognition in real life environment is challenged by various issues such as the object motion, lighting conditions, poses and expressions. In this paper, we present the development of a system based on a refined Enhanced Local Binary Pattern (ELBP) feature set and a Support Vector Machine (SVM) classifier to perform face recognition in a real life environment. Instead of counting the number of 1's in ELBP, we use the 8-bit code of the thresholded data as per the ELBP rule, and then binarize the image with a predefined threshold value, removing the small connections on the binarized image. The proposed system is currently trained with several people's face images obtained from video sequences captured by a surveillance camera. One test set contains the disjoint images of the trained people's faces to test the accuracy and the second test set contains the images of non-trained people's faces to test the percentage of the false positives. The recognition rate among 570 images of 9 trained faces is around 94%, and the false positive rate with 2600 images of 34 non-trained faces is around 1%. Research work is progressing for the recognition of partially occluded faces as well. An appropriate weighting strategy will be applied to the different parts of the face area to achieve a better performance.

The pipeline industry has millions of miles of pipes buried along the length and breadth of the country. Since none of the areas through which pipelines run are to be used for other activities, it needs to be monitored so as to know whether the right-of- way (RoW) of the pipeline is encroached upon at any point in time. Rapid advances made in the area of sensor technology have enabled the use of high end video acquisition systems to monitor the RoW of pipelines. The images captured by aerial data acquisition systems are affected by a host of factors that include light sources, camera characteristics, geometric positions and environmental conditions. We present a multistage framework for the analysis of aerial imagery for automatic detection and identification of machinery threats along the pipeline RoW which would be capable of taking into account the constraints that come with aerial imagery such as low resolution, lower frame rate, large variations in illumination, motion blurs, etc. The proposed framework is described from three directions. In the first part of the framework, a method is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. This method makes use of monogenic phase features into a cascade of pre-trained classifiers to eliminate unwanted regions. The second part of the framework is a part-based object detection model for searching specific targets which are considered as threat objects. The third part of the framework is to assess the severity of the threats to pipelines in terms of computing the geolocation and the temperature information of the threat objects. The proposed scheme is tested on the real-world dataset that were captured along the pipeline RoW.

A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �

There are millions of miles of pipes buried along the length and breadth of the United States. The areas through which these pipelines run cannot be used for other activities. Furthermore, machinery—such as construction equipment and heavy vehicles—are major threats to pipeline infrastructure. Monitoring is therefore required to know whether a pipeline’s right-of-way is threatened at any time. Rapid advances in sensor technologies have enabled the use of high-end video acquisition systems to monitor the right-of-way of pipelines and have generated a huge amount of data. It is, however, very costly to employ analysts to scan through this data and to identify right-of-way threats. An automated mechanism that can detect these threats and issue warnings is therefore warranted. Several object detection and recognition algorithms have been proposed previously.1–5 Car body edges, edges of a front windshield, and shadows can be used as features for car detection.6 An alternative simple vehicle detection algorithm involves exploring four elongated edge operators.7 A top-down matching method has also been developed for vehicle detection from high-resolution aerial imagery.8 On-line boosting, with an interactive training framework, is another option for automatic car detection.9 A feature-based approach to car detection uses scale-invariant transform features and an affinity propagation algorithm.10 We previously designed a three-stage pattern recognition framework to detect construction equipment in various lighting conditions and different object orientations using monogenic signal representation.11, 12 The majority of these techniques, however, are either computationally expensive or unable to deal with the complex environments associated with aerial Figure 1. A sample result of the local textural features-based segmentation (LTFS) algorithm. (a) The original red, green, blue (RGB) image. (b) The LTFS output. The yellow circle indicates the threat object that has been identified.

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Four databases were used for testing the performance of the proposed system: Yale Face database, Extended Yale Face database B, Japanese Female Facial Expression database, and CMU AMP Facial Expression database. The experimental results in all four databases show the effectiveness of the proposed system. Also, the computation cost is lower because of the simplified calculation steps. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose-varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.

We present an object detection algorithm to automatically detect and identify possible intrusions such as construction vehicles and equipment on the regions designated as the pipeline right-of-way (ROW) from high resolution aerial imagery. The pipeline industry has buried millions of miles of oil pipelines throughout the country and these regions are under constant threat of unauthorized construction activities. We propose a multi-stage framework which uses a pyramidal template matching scheme in the local phase domain by taking a single high resolution training image to classify a construction vehicle. The proposed detection algorithm makes use of the monogenic signal representation to extract the local phase information. Computing the monogenic signal from a two dimensional object region enables us to separate out the local phase information (structural details) from the local energy (contrast) thereby achieving illumination invariance. The first stage involves the local phase based template matching using only a single high resolution training image in a local region at multiple scales. Then, using the local phase histogram matching, the orientation of the detected region is determined and a voting scheme gives a certain weightage to the resulting clusters. The final stage involves the selection of clusters based on the number of votes attained and using the histogram of oriented phase feature descriptor, the object is located at the correct orientation and scale. The algorithm is successfully tested on four different datasets containing imagery with varying image resolution and object orientation.

A new shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed in this paper for invariant face recognition while accommodating expression and illumination variations. The enhanced local binary pattern operator is utilized in the preprocessing stage for facial feature extraction. A phase-shifted and phase-encoded fringe-adjusted joint transform correlator (FJTC) is implemented in order to remove any overlapping patterns among the input scene objects and to better utilize the space bandwidth product, which are major drawbacks of classical JTC. Test results obtained using the Yale Face Database and the extended Yale Face Database B is presented to verify the efficiency of the proposed technique. Test results are then compared with alternate JTC techniques such as shifted phase-encoded fringe-adjusted joint transform correlation.