The Aims of the Prophetic Hijrah and its Teaching and Guidance

The aim of Hijrah is not to run away from problem that occurred in the process of giving da'wah, but rather to establish the resolve in solving the problem. Hijrah as a movement carried out by the Prophet Muhammad (PBUH) and his companions from Makkah to Madinah, aimed to keep, maintain and uphold the message of Allah, in the form of Islamic aqeedah and shari’a, in order to achieve the mercy and pleasure of Allah SWT. This move, as we can see in the seerah, later brought a great impact to the success of the Islamic da’wah which was increasingly evolving. Hence the fundamental problem that has led to the migration has been solved. In fact, the Hijrah brought a significant impact not just to the Islamic world but also to world civilisation. The story and background of the migration of Rasulullah (PBUH) from Makkah to Madinah is touched in this writing to show that there were a lot of lessons and guidance that can be inferred. It did not merely signify a final destination for Makkah Muslims, but was also the beginning of a continuous effort to establish a strong and resilient ummah. The event of Hijrah in fact had changed the world after that forever. It highlighted the perseverance of the Rasulullah (PBUH) and the early Muslims through the difficult times. Hijrah demonstrates that for people with faith, there is hope for ease after difficulties. There was also a great unity model among Muslims of different backgrounds. The Hijrah had also marked the beginning of the Islamic State under the leadership of Rasulullah (PBUH) which then became a reference for all state governance everywhere in the future

View-Based Biometrics

Biometric recognition systems are considered to be one of the most secured means of authentication. In this context several biometrics have been proposed but the view based biometrics such as face, iris etc remain the most natural choice. In the paradigm of face recognition, it is generally assumed that major information contents lie in the lower frequency region of an image and therefore little effort has been made in sys tematic exploration of the detail images. Although some wrapper-based approaches have been proposed in the literature, they are primarily based on experimental eval uation of a specific classifier on various subbands. Therefore there is a dire need of a framework for automatic selection of the most significant subbands based on the underlying statistics of the data. In this thesis, the problem of identifying the most dis criminant subbands based on information theoretic measures is addressed. Essentially the face images are transformed into textures using the linear binary pattern (LBP) ap proach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband images. We propose to use the energy features to effectively represent these subband images. The underlying statistical patterns of the data are harnessed in form of information-theoretic metrics to select the most discriminant subbands. The proposed algorithms are extensively evaluated on several standard databases and are shown to always pick the most significant subbands resulting in better performance. The proposed algorithms are entirely generic and do not depend on the validation re sults for specific classifiers. Noting that localized features are often more useful than theholisticapproaches, wehavealsotargetedtheproblemofirisrecognitionproposing the concept of class-specific dictionaries. Essentially, the query image is represented as a linear combination of training images from each class. The well-conditioned inverse problem is solved using least squares regression and the decision is ruled in favor of the class with the most precise estimation. An enhanced modular approach is further proposed to counter noise due to imperfect segmentation of the iris region. As such iris images are partitioned and individual decisions of all sectors are fused using an efficient fusion algorithm. The proposed algorithm is compared to the state-of-the-art Sparse Representation Classification (SRC) with Bayesian fusion for multiple sectors. The proposed approach has shown to comprehensively outperform the SRC algorithm on standard databases. Complexity analysis of the proposed algorithm shows decisive superiority of the proposed approach.