Face age progression and regression have accumulated significant dynamic research enthusiasm because of its gigantic effect on a wide scope of handy applications including finding lost/wanted persons, cross-age face recognition, amusement, and cosmetic studies. The two primary necessities of face age progression and regression, are identity preservation and aging exactitude. The existing state-of-the-art frameworks mostly focus on adult or long-span aging. In this work, we propose a child face age-progress and regress framework that generates photo-realistic face images with preserved identity.To facilitate child age synthesis, we apply a multi-scale patch discriminator learning strategy for training conditional generative adversarial nets (cGAN) which in-creases the stability of the discriminator, thereby making the learning task progressively more difficult for the generator. Moreover, we also introduce Self-Attention Block (SAB) to learn global and long-term dependencies within an internal representation of a child’s face. Thus, we present coarse-to-fine Self-Attention Multi-Scale Patch generative adversarial nets (SAMSP-GAN) model. Our new objective function, as well as multi-scale patch discrimination and, has shown both qualitative and quantitative improvements over the state-of-the-art approaches in terms of face verification, rank-1 identification, and age estimation on benchmarked children datasets.

Secret sharing scheme is an efficient method of transmitting one or more secret images securely. The traditional visual secret sharing schemes share only one secret image at a time. With the advancement of time, there arises a need for sharing more than one secret image. An (n, n) Multi Secret Image Sharing (MSIS) scheme is used to encrypt n secret images into n meaningless shared images and stored it in different database servers. For recovery of secrets n shared images are required. If loss of any shared image then no secret images are recovered. In earlier work n secret images are shared among n or n+1 shared images, which has a problem as one can recover partial secret information from n -- 1 or fewer shared images. Therefore, we need a more efficient and secure (n, n)-MSIS scheme so that less than n shared images does not reveal any information of secret images. In this paper, we propose an three different methods for (n, n)-MSIS scheme. First and second methods of an (n, n)- MSIS scheme, we used XOR and reverse bit operation for grayscale and colored images. In third method of an (n, n)-MSIS scheme, we used the concept of additive inverse (relative to an addition operation) and reverse bit operation for grayscale and colored images. The experimental results show that the proposed schemes requires minimal computation time for encryption and decryption. For quantitative analysis a correlation and Root Mean Square Error (RMSE) techniques are used. The proposed (n, n)-MSIS scheme outperforms the existing state-of-the-art techniques.

This article introduces a large handwritten text document image corpus dataset for Urdu script named CALAM (Cursive And Language Adaptive Methodologies). The database contains unconstrained handwritten sentences along with their structural annotations for the offline handwritten text images with their XML representation. Urdu is the fourth most frequently used language in the world, but due to its complex cursive writing script and low resources, it is still a thrust area for document image analysis. Here, a unified approach is applied in the development of an Urdu corpus by collecting printed texts, handwritten texts, and demographic information of writers on a single form. CALAM contains 1,200 handwritten text images, 3,043 lines, 46,664 words, and 101,181 ligatures. For capturing maximum variance among the words and handwritten styles, data collection is distributed among six categories and 14 subcategories. Handwritten forms were filled out by 725 different writers belonging to different geographical regions, ages, and genders with diverse educational backgrounds. A structure has been designed to annotate handwritten Urdu script images at line, word, and ligature levels with an XML standard to provide a ground truth of each image at different levels of annotation. This corpus would be very useful for linguistic research in benchmarking and providing a testbed for evaluation of handwritten text recognition techniques for Urdu script, signature verification, writer identification, digital forensics, classification of printed and handwritten text, categorization of texts as per use, and so on. The experimental results of some recently developed handwritten text line segmentation techniques experimented on the proposed dataset are also presented in the article for asserting its viability and usability.

The efficiency of any character recognition technique is directly dependent on the accuracy of the generated feature set that could uniquely represent a character and hence correctly recognize it. This article proposes a hybrid approach combining the structural features of the character and a mathematical model of curve fitting to simulate the best features of a character. As a preprocessing step, skeletonization of the character is performed using an iterative thinning algorithm based on Raster scan of the character image. Then, a combination of structural features of the character like number of endpoints, loops, and intersection points is calculated. Further, the thinned character image is statistically zoned into partitions, and a quadratic curve-fitting model is applied on each partition forming a feature vector of the coefficients of the optimally fitted curve. This vector is combined with the spatial distribution of the foreground pixels for each zone and hence script-independent feature representation. The approach has been evaluated experimentally on Devanagari scripts. The algorithm achieves an average recognition accuracy of 93.4%.

Video partitioning may be involve in a number of applications and present solutions for monitoring and tracking particular person trajectory and also helps in to generate semantic analysis of single entity or of entire video. Many recent advances in object detection and tracking concern about motion structure and data association used to be assigned a label to trajectories and analyze them independently. In this work we propose an approach for video portioning and a structure is given to store motion structure of target set to monitor in video. Spatio-temporal tubes separate individual objects that help to generate semantic analysis report for each object individually. The semantic analysis system for video based on this framework provides not only efficient synopsis generation but also spatial collision where the temporal consistency can be resolved for representation of semantic knowledge of each object. For keeping low computational complexity trajectories are generated online and classification, knowledge representation and arrangement over spatial domain are suggested to perform in offline manner.

This paper presents a methodology for the development of an Urdu handwritten text image Corpus and application of Corpus linguistics in the field of OCR and information retrieval from handwritten document. Compared to other language scripts, Urdu script is little bit complicated for data entry. To enter a single character it requires a combination of multiple keys entry. Here, a mixed approach is proposed and demonstrated for building Urdu Corpus for OCR and Demographic data collection. Demographic part of database could be used to train a system to fetch the data automatically, which will be helpful to simplify existing manual data-processing task involved in the field of data collection such as input forms like Passport, Ration Card, Voting Card, AADHAR, Driving licence, Indian Railway Reservation, Census data etc. This would increase the participation of Urdu language community in understanding and taking benefit of the Government schemes. To make availability and applicability of database in a vast area of corpus linguistics, we propose a methodology for data collection, mark-up, digital transcription, and XML metadata information for benchmarking.