حافظ محمد ابراہیم
افسوس ہے کہ گذشتہ مہینے حافظ محمد ابراہیم صاحب نے وفات پائی، مرحوم پرانے قوم پرور اور اس دور کے کانگریسی تھے جس کی یادگاریں اب بہت کم پائی ہیں، اپنے حسن اخلاق اور خدمت کی بنا پر اپنے حلقہ میں بڑے مقبول اور اپنا ذاتی اثر رکھتے تھے، انھوں نے لیگ اور کانگریس کے بڑے معرکے کے الیکشن میں لیگی امیدوار کے مقابلہ میں کامیابی حاصل کی اور کانگریس کا وقار قائم کیا اتر پردیش اور مرکزی حکومت میں وزیر رہے، آخر میں کچھ دنوں تک گورنری بھی کی، اگرچہ وہ بڑے وفادار کانگریسی تھے، لیکن آخر میں ان کو خود اپنی جماعت سے دھوکا اٹھانا پڑا اور ان کی زندگی کا آخری دور پریشانی میں گذرا۔
مرحوم بڑے مرنجان مرنج تھے، مسلمانوں کے معاملات میں بھی ان کی روش یہی تھی، انھوں نے نہ ان کو فائدہ پہنچایا نہ نقصان، ان معاملات میں وہ خاموشی سے کام لیتے تھے، بڑے خوش اخلاق تھے، ہر شخص کو اپنی باتوں سے خوش کرنے کی کوشش کرتے تھے، جب دارالمصنفین کے لوگوں سے ملاقات ہوتی تو بڑی خصوصیت ظاہر کرتے، ایک مرتبہ یہاں تک آمادگی ظاہر کی کہ وہ خود دارالمصنفین آکر اس کی مالی حالت مضبوط کرنے کی کوشش کریں گے مگر اس کی نوبت نہ آسکی، اب قوم پرور مسلمانوں میں ایسے لوگ نہ ملیں گے، ان کی وفات سے ایک پرانی یادگار مٹ گئی اﷲ تعالیٰ ان کی مغفرت فرمائے۔ (شاہ معین الدین ندوی، فروری ۱۹۶۸ء)
Colonialism has impacted on Islamic Law as well as Islamic civilization. Islam has a dissonant relationship with modernity in that it agrees with central aspects of this epochal phenomenon and parts ways with others. The compatibility issues were raised by the Muslim thinkers. Many Muslim scholars have contributed to give an original understanding of message of Islam detached from sectarian influences. Traditions are considered to be the vehicle for the transference of factors to modernity with a strong role in determining power and culture of a civilization. Thus, they pave the way for the power tools of a civilization. The theories of Muslim scholars from Asia and Egypt are discussed to elaborate the Muslim understanding of facing the challenges of modernity. The contemporary scholars’ views are also added with an analysis of their observation on compatibility of Islam with modern era challenges and the criticism on it due to their modernity conception.
Facial expressions are the most effective form of non-verbal communication which is used to express one’s emotions. It not only expresses our expressions, but also communicates a wealth of information during social interaction. Facial expressions play an important and effective role in Human-Computer Interaction. Unlike humans, facial expression recognition is a challenging task for computers and research work in this domain can still be considered in seminal form. The prevalent facial expression techniques tend to provide accurate and impressive results for the images captured under controlled environment with a cooperative subject. However, majority of these techniques fail to provide adequate results for the images captured in uncontrolled environment. Achieving a higher classification accuracy rate for the images captured in uncontrolled environments is a major challenge being faced by researchers in this domain. There are many factors such as illumination, image orientations, noise and low-resolution, which hinder the development of facial expression recognition system in uncontrolled environment. Even the datasets prepared under the constrained environments sometimes need preprocessing before the classification stage. The core objective of this dissertation is to analyze existing techniques and develop an effective framework that is capable of classification of human facial expressions both under constrained and unconstrained environments. In this dissertation, we have explored, investigated and proposed frameworks to deal with two of the most common, influential and challenging issues that affect the accuracy of a facial expression recognition for images captured in an uncontrolled environment, namely illumination and low-resolution. First, a novel framework for feature extraction named Weber Local Binary Image Cosine Transform has been developed which not only extracts significant features by integrating features extracted using local binary pattern and weber local descriptor but also utilizes most discriminant features by applying the frequencybased components. Low-resolution and multi-orientation facial images have been used for the classification and a significant improvement in the classification accuracy rate has been achieved with the proposed framework. The developed framework has proved to be not only reliable but also computationally efficient across multiple datasets in the presence of noise and orientations. The Proposed framework has been tested on four datasets including JAFEE, MMI, CK+ and SFEW datasets. The second major contribution is the development of an illumination invariant technique. The proposed framework has been named Weber Local Binary Image Cosine Transform (WLBI-CT) and it advocates for the need of simultaneous contrast enhancement and brightness preservation for datasets containing real world images in unconstrained environment. The empirical results for the SFEW dataset are promising. In order to evaluate the performance of the proposed framework, rigorous set of experiments are presented in this thesis. The empirical results meet the standard quantitative measure criteria. The comparison of our work with various other stateof-the-art techniques is also presented using various benchmarks for these factors. The results are impressive even in the case of inclusion of noise and occlusion effects.