Abstract Age and gender are the two key attributes for healthy social interactions, access control, intelligence marketing etc. Likewise, carried object recognition helps in identifying owner of the baggage being abandoned or the person littering in the public places. The above‐mentioned surveillance task displays discriminative characteristics in gait. Primates can accomplish scene context understanding and reacting to different circumstances with varying reflexes with ease. Human beings achieve this by recollecting prior experiences and adapting to new situations quickly. Modelling the human behaviour, this research work has combined customized and learnable filters so that knowledge database can always be kept up to date, as well as, provides flexibility in learning new contexts. Thus, a specialized parallel deep convolutional neural network architecture with customized filters that extracts intrinsic characteristics and data driven learnable filters are fused to enhance the performance of single convolutional neural network is proposed. From the experimentation it is observed that, the learning is augmented when customized filters and learnable filters are fused together. Results show that the proposed system achieves better performance for CASIA B datAQ2abase and OU‐ISIR gait database‐large population dataset with age and real‐life carried object.

Abstract Steganography, a branch of data hiding techniques aims to hide confidential information within any digital media by obscuring the existence of hidden information. On the contrary, steganalysis aims to detect steganography. With the advent of powerful steganographic algorithms, the process of cracking them became very challenging. Traditional steganalysis following machine learning principle employs a two‐step process, with first process extracting highly sophisticated features capable of discriminating hidden message from original data and second process classifying the input as innocent or guilty. In recent years, deep learning which has its roots in artificial neural networks emerged as a brilliant alternative for many computer vision tasks. A review of recent research works in deep learning based digital image steganalysis is presented here. The paradigm shift from machine learning approaches to employing more promising deep learning architectures, observed with the current research community and hence in literature has been presented here in chronological order. Deep learning can unify the two‐step process into a single process by giving the ability for machine to learn end‐to‐end by itself. The use of Convolutional Neural Networks to perform steganalysis in spatial or transform or combination of both domains has effectively lowered the detection error rates.

Image fusion is essential to produce a complementary and comprehensive image, with the source images derived from different sensors, captured from different illumination conditions. In this study, a novel multi‐scale image fusion based on the combination of non‐subsampled contourlet transform (NSCT) and rolling‐guidance filter (RGF) is used to enhance the edges and texture details better than the conventional methods. Initially, infrared (IR) and visible (VIS) source images are multi‐scale decomposed to low‐frequency and high‐frequency sub‐band coefficients by NSCT for the best representation of edges and curves. Further, the low‐frequency coefficients are decomposed into the base and detail layers by a combination of RGF and GF (Gaussian filter) to retain the features in multiple scales and to reduce halos near the edges. Base layers are fused by saliencybased fusion rule and detail layers are fused by Max absolute rule. High‐frequency coefficients are fused by consistency verification based fusion rule to preserve visual details and to suppress noise from source images. Finally, the image is reconstructed by inverse NSCT with good visual perception. Experimental results are evaluated by different evaluation metrics and the results suggest that the proposed method results with better improved source information, clarity and contrast.

Iris recognition is one of the most reliable personal identification methods. This paper presents a novel algorithm for iris recognition encompassing iris segmentation, fusion of statistical and co-occurrence features extracted from the curvelet and ridgelet transformed images. In this work, the pupil and iris boundaries are detected by using the equation of circle from three points on its circumference. Using Canny edge detection, the iris radius value is empirically chosen based on rigorous experimentation. Eyelash removal is done by using a horizontal 1-D rank filter. Iris normalization is done by mapping the detected iris region from the polar domain to the rectangular domain and the multi-resolution transforms such as curvelet and ridgelet transforms are applied for multi-resolutional feature extraction. The classification is done using Manhattan distance (Md) and multiclass classifier with logistic function and the two results are compared. Here, the benchmark database CASIA-IRIS-V3 (Interval) is used for identification and recognition. It is observed that the ridgelet transform increases the iris recognition rate.

Generic Steganalysis aims to detect the presence of covert communication by identifying the given test data as stego / cover media. Thresholded adjacent pixel differences using different scan paths have been used to highlight feeble embedding artifacts created out of a low rate embedding process. The scan paths normally made use of in the embedding process have been utilized for a steganalytic scheme. A co occurrence matrix derived from thresholded adjacent pixel differences serves as the feature vector aiding detection of stego images carrying very minimal payloads.