Proof of Real-Time Transfer (PoRT) consensus is a secure, highly scalable, decentralized data verification protocol that supports the exchange of data in any form factor (remaining content agnostic) with guarantees towards data privacy, data integrity and source authentication. It is built on a modular framework that serves core functionalities towards decentralized public key infrastructure (DPKI), message standardization and encryption, decentralized message verification a nd storage. PoRT's security guarantees stem from its adoption of cryptographic primitives and their nuanced derivatives that confer verifiability and further game theoretic constructs that prove pertinence in work. High scalability of PoRT can be attributed to network sharding and compute sharding that optimize resource usage and to parallel chains that confer instant finality. PoRT allows applications to define and implement purpose-built message-driven business solutions across industries like IoT, social media, instant messaging and gaming. Our analysis indicates that the proposed consensus protocol can achieve throughputs upto 976K messages per second on today's commodity hardware.

Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we study the problem by designing and evaluating discriminative approaches. These directly tackle verification tasks without explicit age modeling, which is a hard problem by itself. First, we find that the gradient orientation, after discarding magnitude information, provides a simple but effective representation for this problem. This representation is further improved when hierarchical information is used, which results in the use of the gradient orientation pyramid (GOP). When combined with a support vector machine GOP demonstrates excellent performance in all our experiments, in comparison with seven different approaches including two commercial systems. Our experiments are conducted on the FGnet dataset and two large passport datasets, one of them being the largest ever reported for recognition tasks. Second, taking advantage of these datasets, we empirically study how age gaps and related issues (including image quality, spectacles, and facial hair) affect recognition algorithms. We found surprisingly that the added difficulty of verification produced by age gaps becomes saturated after the gap is larger than four years, for gaps of up to ten years. In addition, we find that image quality and eyewear present more of a challenge than facial hair.

Facial appearances change with increase in age. While generic growth patterns that are characteristic of different age groups can be identified, facial growth is also observed to be influenced by individual-specific attributes such as one's gender, ethnicity, life-style etc. In this paper, we propose a facial growth model that comprises of transformation models for facial shape and texture. We collected empirical data pertaining to facial growth from a database of age-separated face images of adults and used the same in developing the aforementioned transformation models. The proposed model finds applications in predicting one's appearance across ages and in performing face verification across ages.

We propose a two fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face recognition and facial appearance prediction across aging.

In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.

We propose a craniofacial growth model that characterizes growth related shape variations observed in human faces during formative years. The model draws inspiration from the ‘revised’ cardioidal strain transformation model proposed in psychophysical studies related to craniofacial growth. The model takes into account anthropometric evidences collected on facial growth and hence is in accordance with the observed growth patterns in human faces across years. We characterize facial growth by means of growth parameters defined over facial landmarks often used in anthropometric studies. We illustrate how the age-based anthropometric constraints on facial proportions translate into linear and non-linear constraints on facial growth parameters and propose methods to compute the optimal growth parameters. The proposed craniofacial growth model can be used to predict one’s appearance across years and to perform face recognition across age progression. This is demonstrated on a database of age separated face images of individuals under 18 years of age.

Human faces undergo considerable amounts of varialions with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%.

Human faces undergo considerable amount of variations with aging. While studies have revealed the extent to which factors such as illumination variations, pose variations, facial expression and occlusions affect face recognition, the role of natural factors such as aging effects in affecting the same are yet to be studied. How does age progression affect the similarity between two images of an individual? What is the confidence associated with establishing the identity between two age separated face images of an individual? On a database of pairs of passport images, we study similarity of faces as a function time. We propose a Bayesian age-difference classifier that is built on a probabilistic eigenspaces framework. Since age separated face images invariably differ in illumination and have facial variations due to aging, we propose a method to overcome non uniform illumination across face images. The problem discussed in this paper has direct applications in passport renewal and homeland security.