3D face recognition has made considerable progress during the last decade as an emerging biometric modality. In order to ensure reliable 3D face recognition, novel 3D alignment and recognition algorithms are proposed in this research work. The principal objective of this dissertation is to investigate and introduce novel techniques to construct a fully automatic 3D facial recognition system. The first study presents a novel, pose and expression invariant approach for 3D face alignment based on intrinsic coordinate system (ICS) 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 the proposed alignment 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. The individual regions are classified using Mahalanobis Cosine (MahCos) distance, Euclidean distance, Mahalanobis (Mah) distance, and Manhattan distance in separate experiments. The resulting matching scores are combined using weighted Borda Count (WBC) based combination and a re-ranking stage. The performance of the proposed approach is corroborated by extensive experiments performed on two databases, namely, FRGC v2.0 and GavabDB, confirming effectiveness of fusion strategies to improve performance. In the second study, a novel and fully automatic pose and expression invariant 3D face recognition algorithm is proposed using two-pass 3D face alignment based on minimum distance and two-pass 3D face alignment based on classification approach. The proposed alignment approaches are capable of aligning neutral and expressive 3D faces acquired at frontal and non-frontal poses whereas the former is capable of aligning profile face images as well. For the face recognition framework, multi-view 3D faces are synthesized to exploit real 3D facial information. The matching scores are computed between multi-view face images using Mahalanobis Cosine (MahCos) viii distance, Euclidean distance, Mahalanobis (Mah) distance and Manhattan distance in separate experiments. Inspired by the effectiveness of fusion approaches, Support Vector Machine (SVM) is employed using scores obtained from multi-view face pairs for face verification. In addition, a three stage unified classifier based face identification algorithm is employed which combines results from seven base classifiers at first stage, two parallel face recognition algorithms at second stage and an exponential rank combiner at third stage in a hierarchical manner. For profile face images, the face identification algorithm combines results using four base classifiers, two parallel face recognition algorithms and the rank combiner stage. The performance of the proposed methodology is demonstrated by extensive experiments performed on two databases: FRGC v2.0 and GavabDB. The results show that the proposed methodology can be efficiently used to construct a pose and expression invariant facial recognition system.
آخرت پر ایمان لانے کا مطلب یہ ہے کہ وفات کے بعد سے لے کر جنت یا جہنم میں داخل ہونے کے بارے میں قرآن اور حدیث میں میں تمام احکامات کی تصدیق کرنا اور اس پر یقین رکھنا کیونکہ کہ عقیدہ آخرت شرط ارکان ایمان کا اہم جز ہے اور اس پر ایمان لائے بغیر مکمل مومن نہیں ہو سکتے، اس لیے اسلام میں عقیدہ آخرت کی بہت اہمیت ہے ۔ اس فصل میں تفسیر تفہیم القرآن قرآن کی روشنی میں آیت استفہام اور عقیدہ آخرت کی وضاحت کی گئی ہے جو کہ درج ذیل آیات ہیں:
Jihad is a sacred term and a sacred endeavor as well, but unfortunately it has been misunderstood and misused in many cases, therefore its elaboration and explanation is needed from various aspects. This article deals with the principles and rules of Jihad. At the beginning, the concept of jihad has been expounded, then motivations of war of the period of ignorance have been discussed. Those motivations have been brought in to focus which Islam has recognized. For instance defending of religion and faith, safeguarding the territory of Muslims, eradication of persecution, eradication of Internal enemies, support for the oppressed Muslims etc. The purpose of jihad has also been discussed, while principles and rules of jihad make the next part of the article. In this regard views of a well-known medieval Hanafi jurist Imam Hasan al- Shaybani with the commentary of contemporary scholars have been produced.
Time-aligned and labeled speech at sub-word level is required to develop spoken language technology components. Determining time boundaries of sub word units of speech and labelling those, is the speech segmentation problem. Manual human-labeling is considered to be the most accurate, which however requires significant amount of time when large amount of speech has to be dealt. The evidences which humans employ are based on knowledge of acoustic-phonetics and at very basic level works on spectrograms based techniques. Based on a hypothesis that computers can also segment speech automatically if evidence which human experts utilizes are used, leads us towards time effective automatic speech segmentation. In this thesis unsupervised automatic time-alignment of speech at sub-word level is carried out based on the pieces of information which spectrograms carry. The speech spectrogram engineered in this thesis does not possess information of vocal excitations and capture dynamics of vocal tract only. The novel feature is found suitable for segmentation problem and utilizes both forward and inverse characteristics of vocal tract (FICV). Additionally to evaluate the suitability of a feature extraction technique for speech segmentation task, a framework has also been developed. In the thesis, speech segmentation is carried out on indigenously developed Classical Arabic (CA) dataset and therefore becomes first scheme of its kind for CA which is an under resourced language in speech technology. The performance of FICV based speech segmentation scheme is compared and shown to be significantly better than standard unsupervised and supervised techniques both in terms of error-rates and alignment accuracies. Reduction of 12.29% in error rates is achieved with FICV based feature when compared with standard unsupervised technique. Carrying out supervised segmentation requires a basic sub-word level recognizer, which labels and aligns speech. In this connection a Hidden Markov Model (HMM) based speech recognizer is trained. The acoustic modeling is carried using a discriminative technique which shows better recognition accuracies of up to 4% than the non-discriminative technique. Thesis also verifies that using manually-labeled data for training acoustic models can further improve recognition accuracies by 3-4%. In this regard, thesis carries details of experimental steps which can also serve as guideline for developing an automatic speech recognizer for CA.