دوست کی فرمائش
بڑا افسوس ہے تیری بے وفائی کا
صدا غم رہے گا تیری اس جدائی کا
جس باغیچے کو ہم نے پرورش کیا
کسی موسم میں بھی سوکھنے نہ دیا
پھولوں کے عین شباب میں جدا کردیا
بڑا امتحان تھا میری صبر آزمائی کا
پھولوں کے نکھار نے دل میں بسیرا کیا
اندھیرا تھا دل میں سویرا کیا
جدائی کے غم نے برا حال میرا کیا
درد اٹھتا رہے گا دل پہ چوٹ لگائی کا
پھولوں کے نکھار پہ بلبلیں بھی آنے لگیں
جھرمٹ بناکے وہ گیت گانے لگیں
پھولوں پہ بیٹھ کے وہ خوشیاں منانے لگیں
بڑا دکھ ہوا پھولوں کی خوشبو چرائی کا
مالی نے کی پھولوں کی بہت ہی رکھوالی
مگر لالچ میں آکر توجہ ہٹالی
صورت حال گئی نہ اس سے سنبھالی
بڑا چرچا ہوا اس کی رسوائی کا
چاولہ سائیں پھولوں کی مہک ہے بہت نرالی
بلبلوں کو بھی مل جائے گی اک دن دیس نکالی
چمن رہ جائے گا سب خالی کا خالی
تجھے کیا ملے گا دنیا میں دل بہلائی کا
A major proof viewed by the Islamic universe today is the proof of re-making and reorganizing the economy in such a way seen as similar to the motivation of Sharia’h work. Money-related new development is one of the topics that in a general sense is termed as a science that offers a technique to show the science of monetary angles. Nevertheless, the standard technique, which has been assembled, is being considered for dissatisfaction in dealing with the issues of the Ummah, for example, despair, backwardness in human resources (HR), and achievement in proper position and life, and even a matter of natural change. Sadly, those conditions are found overwhelmingly in the Muslim world, where they have essentially manifested and represented in Muslim life. Moreover, it seems that the uniqueness of the Muslim world has been challenging, that is, it is typically rich in asset but still weak and poor. As for the path, in Muslim society, there is an ignorance of the achievements of motivation. Thus, there is no possible norm to find the emergence of different methodologies within the Islamic structure, surprisingly in the area of money-related to unforeseen development.
Face recognition, as an active research area over the past three decades, still poses many challenges. Recognition of age-separated face images (age invariant face recognition) based on facial asymmetry is one of such challenges. Successful solutions to this recognition paradigm would allow the facial photographs to be matched against face images with temporal variations. Facial asymmetry, which refers to non-correspondence in shape, size, and arrangement of facial landmarks on both sides of the face, is an intrinsic recognition-specific facial feature used for face recognition task. The contributions of this dissertation are focused on recognition of age-separated face images using facial asymmetry. We introduce to use a feature description scheme suitable to represent facial asymmetry. The introduced feature description is adaptable to recognize age-separated face images and extract demographic information such as age group, gender, and race from a given face image. Based on the introduced feature description, this dissertation offers the following three main contributions to recognize age-separated face images. The first contribution is a matching-scores space based approach to recognize age separated face images. In the proposed framework, matching scores of holistic, local, and asymmetric facial features are combined in a matching-score space (MSS) with Support Vector Machine (SVM) as a classifier to separate genuine and imposter classes. Experimental results on three publically available benchmark facial aging databases show the efficacy of proposed approach compared to some existing state-of-the-art approaches. The second contribution is focused on the role of facial asymmetry based age group estimation in recognizing age-separated face images. We provide a hierarchical approach to perform age group estimation task. The role of various asymmetric facial regions in recognizing age-separated face images of different age groups is investigated. We integrate the knowledge learned from age group estimation into face recognition algorithm to enhance the recognition performance of age-separated face images. The viability of this approach is demonstrated on two benchmark facial aging databases. The experimental results suggest that integration of age group estimates into face recognition algorithm enhances the recognition performance of age separated face images, considerably. The third contribution is examination of the role of facial asymmetry in demographic estimation (i.e. age group, gender, and race) of a query face image in a face recognition system. The role of different asymmetric facial regions in recognizing face images with different demographic attributes is presented. We integrate the demographic estimates into a face recognition algorithm to enhance the recognition accuracy of age-separated face images. Experiments are conducted on benchmark facial aging databases to validate the performance of proposed approach. The experimental results suggest that proposed approach is more adaptable to recognize age-separated face images compared to some existing state-of-the-art methods.