Usama Ijaz Bajwa

Comparison of boundary detection techniques to improve image analysis in medical thermography

Abstract In digital imaging, poor contrast between the target and its background can affect the extraction of the object of interest and increase the time used in its analysis. Medical thermal imaging requires the correct interpretation of the thermal values obtained from the region of interest. In this investigation, a subjective and objective comparison of currently available outlining techniques is performed to determine the optimal method. Results indicate that probability-based operators produce the best outcome especially after pre-processing with a noise removal filter. The findings of this study suggest that probability-based edge detection techniques in combination with homomorphic filtering and limited post-processing provide initial estimate delineations of areas. These delineations are of sufficient quality for subsequent automatic or semiautomatic post-processing so that a maximum of the original information inside the regions is preserved without loss or distortion of data.

Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters

The spread of dengue fever depends mainly on the availability of favourable breeding sites for its mosquito vectors around human dwellings. To investigate if the various factors influencing breeding habitats can be mapped from space, dengue indices, such as the container index, the house index and the Breteau index, were calculated from Ministry of Public health data collected three times annually in Phitsanulok, Thailand between 2009 and 2011. The most influential factors were found to be temperature, humidity, rainfall, population density, elevation and land cover. Models were worked out using parameters mostly derived from freely available satellite images and fuzzy logic software with parameter synchronisation and a predication algorithm based on data mining and the Decision Tree method. The models developed were found to be sufficiently flexible to accommodate additional parameters and sampling data that might improve prediction of favourable breeding hotspots. The algorithm applied can not only be used for the prediction of near real-time scenarios with respect to dengue, but can also be applied for monitoring other diseases influenced by environmental and climatic factors. The multi-criteria model presented is a cost-effective way of identifying outbreak hotspots and early warning systems lend themselves for development based on this strategy. The proposed approach demonstrates the successful utilisation of remotely sensed images to map mosquito breeding habitats.

A comprehensive comparative performance analysis of Laplacianfaces and Eigenfaces for face recognition

Abstract This paper provides a comprehensive comparative analysis of the performance of locality preserving projections (LPPs)‐based Laplacianfaces, which is a recently introduced algorithm with the more traditional, principal component analysis (PCA)‐based Eigenfaces. All possible combinations of neighbourhood defining distance metrics, classifier distance metrics and number of retained eigenvectors have been tried on different imaging environments. The FERET facial database was chosen which provides enough diversity in illumination, facial expressions and aging. CsuFaceIdEval, an open source platform, is used for this comparison and recognition rates are studied in detail. As a result of our detailed analysis, we provide best combination of selected parameters to extract the best results from these two algorithms.

The role of facial asymmetry in recognizing age-separated face images

Facial asymmetry based approach is proposed to classify age-separated face images.Facial asymmetry is measured and evaluated across temporal variations.A 3 D matching-scores space is built using holistic, local and asymmetric features.SVM is used as classifier to separate genuine and imposter classes in score space.Results show better performance of proposed approach compared to existing methods. Recognition of age-separated face images is a challenging and open research problem. In this paper we propose a facial asymmetry based matching-score space (MSS) approach for recognition of age-separated face images. Motivated by its discriminatory information, we evaluate facial asymmetry across small and large temporal variations and use asymmetric facial features to recognize age-separated face images. We extract three different facial features including holistic feature descriptors using Principal Component Analysis (PCA), local feature descriptors using Local Binary Patterns (LBP), and Densely Sampled Asymmetric Features (DSAF) to represent face images. Then we develop MSS to discriminate genuine and imposter classes using support vector machine (SVM) as a classifier. Experimental results on three widely used face aging databases, the FERET, MORPH and FG-NET, show that proposed approach has superior performance compared to some existing state-of-the-art approaches. Display Omitted

A multifaceted independent performance analysis of facial subspace recognition algorithms.

Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)2PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.

3D face recognition based on pose and expression invariant alignment

Display Omitted A 3D face registration and recognition approach is proposed.Pose correction is evaluated through various correction parameters.Face range images are divided into different regions and features are extracted.Ensemble classifier is used to fuse results using features from different regions.Results show mark improvement in registration accuracy and recognition rates. In this paper we present a novel pose and expression invariant approach for 3D face registration based on intrinsic coordinate system characterized by nose tip, horizontal nose plane and vertical symmetry plane of the face. It is observed that distance of nose tip from 3D scanner is reduced after pose correction which is presented as a quantifying heuristic for proposed registration scheme. In addition, motivated by the fact that a single classifier cannot be generally efficient against all face regions, a two tier ensemble classifier based 3D face recognition approach is presented which employs Principal Component Analysis (PCA) for feature extraction and Mahalanobis Cosine (MahCos) matching score for classification of facial regions with weighted Borda Count (WBC) based combination and a re-ranking stage. The performance of proposed approach is corroborated by extensive experiments performed on two databases: GavabDB and FRGC v2.0, confirming effectiveness of fusion strategies to improve performance.

Morpheme Matching Based Text Tokenization for a Scarce Resourced Language

Text tokenization is a fundamental pre-processing step for almost all the information processing applications. This task is nontrivial for the scarce resourced languages such as Urdu, as there is inconsistent use of space between words. In this paper a morpheme matching based approach has been proposed for Urdu text tokenization, along with some other algorithms to solve the additional issues of boundary detection of compound words, affixation, reduplication, names and abbreviations. This study resulted into 97.28% precision, 93.71% recall, and 95.46% F1-measure; while tokenizing a corpus of 57000 words by using a morpheme list with 6400 entries.

Facial Asymmetry‐Based Age Group Estimation: Role in Recognizing Age‐Separated Face Images

Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individuals age range based on facial features. Recognizing age‐separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age‐separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age‐assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age‐dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state‐of‐the‐art methods.

Bilateral symmetry detection on the basis of Scale Invariant Feature Transform.

The automatic detection of bilateral symmetry is a challenging task in computer vision and pattern recognition. This paper presents an approach for the detection of bilateral symmetry in digital single object images. Our method relies on the extraction of Scale Invariant Feature Transform (SIFT) based feature points, which serves as the basis for the ascertainment of the centroid of the object; the latter being the origin under the Cartesian coordinate system to be converted to the polar coordinate system in order to facilitate the selection symmetric coordinate pairs. This is followed by comparing the gradient magnitude and orientation of the corresponding points to evaluate the amount of symmetry exhibited by each pair of points. The experimental results show that our approach draw the symmetry line accurately, provided that the observed centroid point is true.

Introducing set of internal parameters for Laplacian faces to enhance performance under varying conditions

Laplacianfaces is a recent addition to appearance based face recognition algorithms with promising future potential. Unlike Eigenfaces algorithm, Laplacianfaces algorithm finds an embedding that preserves the locality information of the subjects in feature space. In this study we have comprehensively evaluated the performance of Laplacianfaces against PCA on FERET face-image database using csuFaceIdEval as the testing platform. The effect of internal parameters, including size of locality to be preserved, the choice of distance measure to determine locality and the number of leading eigenvalues to be used for matching has been thoroughly studied for the first time. The impact of illumination, face expression and age variations on the relative performance of Laplacianfaces and Eigenfaces has been shown to be very significant and best parameter settings for enhanced performance have been proposed.